Feature Strategy

Overview

• Total features: 19

Core Features

High-Volume Ingestion Pipeline with Agent-Side SDK

• ID: core-1
• Priority: high
• Category: core
• Description: A scalable ingestion pipeline that allows agent-side SDKs to push structured telemetry events (tool calls, reasoning steps, state transitions) at enterprise scale with per-tenant isolation and replay capability for historical event reprocessing.
• User impact: Enables engineering teams to reliably and flexibly send telemetry data from AI agents for observability without disrupting existing workflows.

Requirements:

• Agent-side SDK must support modular adapters and flexible APIs for integration with diverse AI agent frameworks like LangChain and custom orchestrators.
• SDK must allow tenants to configure event types to capture, sampling rates, and telemetry data collection policies.
• Ingestion pipeline must support per-tenant data isolation and retention policies.
• Support replay of historical telemetry events for downstream reprocessing.

Technical requirements:

• Must handle high throughput and concurrency for enterprise scale event ingestion.
• Support asynchronous event processing and buffering.
• Ensure data integrity and ordering for replay capability.

Real-Time Detection Layer

• ID: core-2
• Priority: high
• Category: core
• Description: A detection system that performs semantic drift detection using embedding-based comparison against baseline agent behavior, policy violation checks, and behavioral anomaly scoring with tiered latency targets (sub-second for critical alerts and minutes-scale for batch checks).
• User impact: Allows security and engineering teams to detect AI agent behavioral issues and policy violations proactively before impacting customers.

Requirements:

• Support embedding-based semantic drift detection against baseline behavior per tenant.
• Detect policy violations including data privacy, content compliance, security policies, and industry-specific regulations.
• Allow tiered latency detection: sub-second alerts for critical issues and batch processing for less urgent checks.
• Provide alerting mechanisms integrated with customer incident management and security tools.

Technical requirements:

• Must support low-latency processing for near real-time alerting.
• Scalable to handle high-volume telemetry streams.
• Support configurable detection thresholds and rules.

LLM Reasoning Trace Capture

• ID: core-3
• Priority: high
• Category: core
• Description: Capture and store full execution traces of AI agent runs including tool call sequences, intermediate reasoning steps, and contextual metadata explaining agent decisions, enabling replay and forensic comparison.
• User impact: Enables engineering teams and security leaders to understand why AI agents made specific decisions and investigate incidents thoroughly.

Requirements:

• Capture detailed reasoning traces with contextual metadata for each agent execution.
• Store traces in a retrievable format for replay and forensic analysis.
• Support multi-tenant isolation of trace data.

Technical requirements:

• Ensure trace data is indexed and searchable for efficient retrieval.
• Support storage scalability for large volumes of trace data.

Evaluation Layer for Offline and Online Scoring

• ID: core-4
• Priority: high
• Category: core
• Description: Provide both offline evaluation (regression testing new model versions against historical failure memory) and online continuous scoring of live agent outputs against policy and quality baselines.
• User impact: Helps engineering teams validate new AI agent versions and monitor ongoing compliance and quality in production.

Requirements:

• Support offline regression testing using historical failure memory corpus before deployment.
• Continuously score live production agent outputs for policy compliance and quality.
• Allow integration of LLM-as-judge and rule-based policy checks in evaluation.
• Provide configurable evaluation criteria per tenant.

Technical requirements:

• Support batch and streaming evaluation modes.
• Ensure evaluation results are timely and actionable.

Failure Clustering and Taxonomy Management

• ID: core-5
• Priority: high
• Category: core
• Description: Automatically group non-deterministic production incidents into structured canonical failure modes using embeddings and pattern discovery, with user-in-the-loop feedback for refining clusters, manual overrides, and annotations.
• User impact: Prevents duplicate root cause diagnosis and accelerates incident resolution by providing a structured failure knowledge base.

Requirements:

• Automatically cluster recurring AI failures into canonical failure modes using embedding similarity and pattern discovery.
• Allow engineering teams to validate, merge, split, and annotate failure clusters.
• Support manual overrides and contextual annotations to capture root-cause hypotheses.
• Maintain a searchable institutional failure memory per tenant.

Technical requirements:

• Support interactive cluster management with low latency.
• Persist cluster metadata and user feedback for continuous learning.

Governance Dashboard with Role-Based Views

• ID: core-6
• Priority: medium
• Category: core
• Description: Provide role-based dashboard views tailored for CISOs/security leaders (policy compliance, audit trail, risk perimeter) and engineering teams (execution traces, regression alignment, remediation status).
• User impact: Enables different stakeholders to access tailored insights for governance and operational purposes.

Requirements:

• Support role-based access control to display relevant data and metrics per user role.
• Provide views for policy compliance status, audit trails, and risk perimeter for security leaders.
• Provide execution trace exploration, regression test results, and remediation tracking for engineering teams.

Agent-Side SDK Customization and Configuration

• ID: core-7
• Priority: high
• Category: core
• Description: Allow tenants to customize telemetry data collection via SDK by configuring event types, sampling rates, and defining policy rules or semantic thresholds to tailor observability to compliance and operational needs.
• User impact: Provides tenants control over telemetry data collection to balance observability, compliance, and performance.

Requirements:

• Expose APIs or configuration interfaces for tenants to select which telemetry events to capture.
• Allow adjustment of sampling rates for telemetry data collection.
• Enable definition and extension of policy violation detection rules and semantic thresholds.

Technical requirements:

• SDK must be modular and extensible to support diverse AI agent frameworks.
• Configuration changes must propagate without disrupting agent workflows.

Alerting Integration with Incident Management and Security Tools

• ID: core-8
• Priority: high
• Category: core
• Description: Integrate alerting mechanisms with customers’ existing incident management and security platforms via standard protocols such as webhooks, SIEM connectors, or APIs to deliver role-based notifications aligned with enterprise workflows.
• User impact: Enables proactive incident response by integrating alerts into existing enterprise workflows.

Requirements:

• Support alert delivery via webhooks, SIEM connectors, and APIs.
• Allow configuration of alert routing and notification preferences per tenant and user role.
• Ensure alerts include context for rapid incident response.

Technical requirements:

• Support reliable and low-latency alert delivery.
• Ensure secure and authenticated integration endpoints.

Integration Features

OpenAI API Integration

• ID: integration-1
• Priority: high
• Category: integration
• Description: Integration with OpenAI API to support AI/LLM layer functionalities such as semantic drift detection, evaluation scoring, and reasoning trace enrichment.
• User impact: Enables advanced AI capabilities for semantic analysis and evaluation within the platform.

Requirements:

• Support API calls to OpenAI for embedding generation, evaluation, and reasoning trace processing.
• Handle API authentication and rate limits transparently.

Integrations:

• OpenAI

Anthropic API Integration

• ID: integration-2
• Priority: high
• Category: integration
• Description: Integration with Anthropic API to support AI/LLM layer functionalities including semantic drift detection, evaluation, and reasoning trace capture.
• User impact: Provides alternative or complementary AI capabilities to OpenAI integration for enhanced observability.

Requirements:

• Support API calls to Anthropic for embeddings, evaluation, and trace enrichment.
• Manage authentication and API usage limits.

Integrations:

• Anthropic

Incident Management and Security Platform Integrations

• ID: integration-3
• Priority: high
• Category: integration
• Description: Integrations with enterprise incident management and security tools to deliver alerts and notifications using standard protocols.
• User impact: Ensures alerts are delivered into existing enterprise workflows for timely incident response.

Requirements:

• Support integration with platforms such as PagerDuty, Splunk, and ServiceNow via webhooks, SIEM connectors, or APIs.
• Allow tenants to configure which tools to integrate with and how alerts are routed.

Integrations:

• PagerDuty
• Splunk
• ServiceNow

Infrastructure Features

Multi-Tenant Architecture with Data Isolation

• ID: infra-1
• Priority: high
• Category: infrastructure
• Description: Support strict per-tenant data isolation and retention policies to meet enterprise compliance and security requirements.
• User impact: Protects tenant data privacy and compliance, enabling enterprise adoption.

Requirements:

• Ensure tenant data is logically separated and access controlled.
• Implement configurable data retention and compliance policies per tenant.

Support for SaaS and On-Premise/Air-Gapped Deployment Models

• ID: infra-2
• Priority: high
• Category: infrastructure
• Description: Provide deployment flexibility to support fully managed SaaS with continuous updates and integrations, as well as on-premise or air-gapped deployments with limited or manual integration updates and stricter data flow controls.
• User impact: Allows enterprises to choose deployment mode that fits their security and compliance needs.

Requirements:

• SaaS deployment must support full feature set with managed integrations and rapid onboarding.
• On-premise deployment must support self-contained operation with local proxies or manual updates for external integrations.
• Document feature availability differences and trade-offs between deployment modes.

Event-Driven Microservices Architecture with Message Broker

• ID: infra-3
• Priority: high
• Category: infrastructure
• Description: Implement a microservices architecture with an event-driven design using a message broker to separate fast ingestion paths from heavier asynchronous processing.
• User impact: Provides scalable and resilient backend infrastructure to handle high-volume telemetry ingestion and processing.

Requirements:

• Support asynchronous buffering and processing of telemetry events.
• Ensure scalability and fault tolerance of ingestion and processing pipelines.

Non-Functional Requirements

Low-Latency Alerting

• ID: nfr-1
• Priority: high
• Category: nfr
• Description: Ensure critical behavioral drift and policy violation alerts are delivered with sub-second to low-second latency to enable proactive incident response.
• User impact: Allows security and engineering teams to respond quickly to critical AI agent issues.

Requirements:

• Critical alerts must be generated and delivered within sub-second to low-second latency.
• Batch checks may operate on longer intervals (minutes to hours) for trend analysis.

Configurable Policy Violation Detection Rules

• ID: nfr-2
• Priority: high
• Category: nfr
• Description: Support highly flexible policy violation detection rules allowing customers to define custom rules, adjust thresholds, and extend semantic/contextual checks via APIs or user interfaces.
• User impact: Enables tenants to tailor compliance monitoring to evolving regulations and internal policies.

Requirements:

• Allow tenants to create, modify, and extend policy violation detection rules.
• Support adjustment of detection thresholds and semantic similarity parameters.

Scalability for Enterprise-Scale Telemetry

• ID: nfr-3
• Priority: high
• Category: nfr
• Description: Ensure the platform can scale to handle high-volume telemetry ingestion, processing, storage, and querying for large enterprise tenants.
• User impact: Provides reliable observability for large-scale AI agent deployments without degradation.

Requirements:

• Support horizontal scaling of ingestion, processing, and storage components.
• Maintain performance under peak loads.

Data Retention and Compliance Controls

• ID: nfr-4
• Priority: high
• Category: nfr
• Description: Implement configurable data retention policies and compliance controls per tenant to meet enterprise regulatory requirements.
• User impact: Ensures platform usage aligns with legal and regulatory obligations.

Requirements:

• Allow tenants to specify data retention durations and deletion policies.
• Ensure compliance with regulations such as GDPR, CCPA, HIPAA, and FINRA.

Secure and Authenticated API Access

• ID: nfr-5
• Priority: high
• Category: nfr
• Description: Provide secure authentication and authorization mechanisms for all API endpoints, SDKs, and integrations to protect tenant data and platform integrity.
• User impact: Protects sensitive telemetry and compliance data from unauthorized access.

Requirements:

• Implement role-based access control for API and SDK usage.
• Ensure secure authentication for all external integrations.

Date: Saturday, August 1, 2026 Research as of: Saturday, August 1, 2026 (2026-08-01) Version: 1.0.0 Technology research confidence: high

Technology research sources:

• https://test.python.org/downloads/release/python-3146/?utm_source=openai
• https://test.python.org/downloads/?utm_source=openai
• https://releasealert.dev/pypi/fastapi?utm_source=openai
• https://devupdate.io/explore/fastapi/fastapi?utm_source=openai
• https://test.python.org/doc/versions/
• https://github.com/tiangolo/fastapi
• https://fastapi.tiangolo.com/

AI Agent Trust Monitor – Enterprise-Grade Technical Architecture

1. SYSTEM OVERVIEW

High-Level System Design

The AI Agent Trust Monitor is a SaaS API service (with on-premise/air-gapped support) for ingesting, processing, and analyzing telemetry from enterprise AI agents. It provides real-time detection of behavioral drift and policy violations, maintains a structured "failure memory" for incident clustering, and supports interactive evaluation and governance workflows. The system is built on an event-driven microservices architecture with strict multi-tenancy, supporting high-throughput ingestion and low-latency alerting.

Key Components:

• Agent-Side SDK: Modular client for structured telemetry push, configurable per tenant.
• Ingestion Gateway: Async, high-throughput API accepting telemetry events, authenticating tenants, and pushing to the message broker.
• Message Broker: Durable, partitioned event bus (Kafka-style) for decoupling ingestion from downstream processing.
• Detection Service: Real-time and batch processing for semantic drift, policy violations, and anomaly scoring.
• Trace Repository: Stores full reasoning traces, indexed for replay and search.
• Evaluation Service: Runs offline regression tests and online scoring using historical failure memory and live outputs.
• Failure Clustering Service: Embedding-based clustering, taxonomy management, user-in-the-loop feedback.
• Alerting Service: Integrates with external incident/security tools (webhooks, SIEM, APIs).
• Governance API: Exposes role-based, multi-tenant metrics, audit logs, and compliance data.
• Integration Connectors: Managed adapters for OpenAI, Anthropic, PagerDuty, Splunk, ServiceNow, etc.
• API Gateway: Exposes OpenAPI contracts (FastAPI), enforces auth/rate limits, and routes tenant-scoped traffic.
• Observability Stack: OpenTelemetry, Prometheus, Grafana, and centralized logging.

Data Flows:

• Agent SDK → Ingestion Gateway → Message Broker → {Detection, Trace, Evaluation, Clustering} Services → Alerting/Integration → Governance API

System Boundaries:

• API Service Boundary: All customer/agent interactions via authenticated API endpoints.
• Integration Boundary: Outbound calls to LLM APIs (OpenAI/Anthropic) and incident/security platforms.
• Data Boundary: Strict per-tenant partitioning at storage, processing, and API layers.

Key Architectural Decisions:

• Event-driven microservices for scale and separation of real-time/batch paths.
• Python/FastAPI for async-first, high-performance API services (per team expertise and requirements).
• Kafka-style broker for replayable, ordered event streams.
• PostgreSQL (with pgvector) for transactional data and vector search.
• OpenSearch for fast, multi-tenant search on traces/failure memory.
• Multi-tenancy isolation from day one.
• OpenAPI-first contracts for all APIs.
• Role-based access control (RBAC) at all API and data layers.

flowchart TD
 A["Agent-Side SDK"] --> B["Ingestion Gateway (FastAPI)"]
 B --> C["Message Broker (Kafka-style)"]
 C --> D1["Detection Service"]
 C --> D2["Trace Repository"]
 C --> D3["Evaluation Service"]
 C --> D4["Failure Clustering Service"]
 D1 --> E1["Alerting Service"]
 D1 --> E2["Integration Connectors"]
 D2 --> F1["Trace Search API"]
 D4 --> F2["Failure Memory API"]
 E1 --> G["Incident/Security Platforms"]
 E2 --> G
 H["Governance API (FastAPI)"] --> F1
 H --> F2
 subgraph "Observability & Monitoring"
 Z1["OpenTelemetry Collector"]
 Z2["Prometheus"]
 Z3["Grafana"]
 Z4["Centralized Logging"]
 end
 B --> Z1
 D1 --> Z1
 D2 --> Z1
 D3 --> Z1
 D4 --> Z1
 E1 --> Z1
 H --> Z1

2. TECHNOLOGY STACK

Primary Recommendations (one per category; version pins from web research):

Technology Compatibility:

• All selected components have mature, actively maintained Python libraries.
• Kafka, PostgreSQL, and OpenSearch scale horizontally and are proven in regulated/enterprise settings.
• All core components can be run in both managed cloud (SaaS) and on-premise/air-gapped environments.

3. ARCHITECTURE PATTERNS

Patterns Used

• Event-Driven Microservices:

• All core features are implemented as separately deployable, async-first Python services, communicating via Kafka topics and REST/gRPC APIs.

• Enables clear separation between fast-path (real-time detection) and heavy-path (batch eval, clustering).

• CQRS (Command Query Responsibility Segregation):

• Ingestion services write to event streams; query APIs read from optimized stores (OpenSearch, PostgreSQL).

• Ensures ingestion is never blocked by slow queries.

• Multi-Tenancy with Strict Isolation:

• All data, processing, and API access are tenant-scoped by design, enforced at every layer (DB, broker, API).

Pattern Rationale:

• Event-driven microservices are necessary for decoupling high-throughput ingestion from heavier analytics and for supporting both real-time and batch detection.
• CQRS ensures scale and reliability for both ingestion and analytics.
• Strict multi-tenancy is non-negotiable for enterprise compliance.
• OpenAPI-first accelerates developer onboarding and integration.
• Observability is critical for trust and compliance in enterprise AI.

4. DATABASE DESIGN

Feature-to-Table Mapping

1. High-Volume Ingestion Pipeline with Agent-Side SDK

Tables:

• tenants
• tenant_configs
• telemetry_events
• event_replay_queue

Schema:

• tenants

• id (UUID, PK, DEFAULT gen_random_uuid())

• name (VARCHAR(100), UNIQUE, NOT NULL)

• status (ENUM: active, suspended, deleted, NOT NULL)

• created_at (TIMESTAMP, DEFAULT NOW())

• (INT, NOT NULL, DEFAULT 90)

2. Real-Time Detection Layer

Tables:

• detections
• detection_rules
• detection_alerts
• detection_results

Schema:

• detections

• id (UUID, PK, DEFAULT gen_random_uuid())

• tenant_id (UUID, FK to tenants.id, NOT NULL)

• telemetry_event_id (UUID, FK to telemetry_events.id, NOT NULL)

• detection_type (ENUM: semantic_drift, policy_violation, anomaly, NOT NULL)

• (FLOAT, NOT NULL)

3. LLM Reasoning Trace Capture

Tables:

• agent_traces
• trace_steps
• trace_metadata

Schema:

• agent_traces

• id (UUID, PK, DEFAULT gen_random_uuid())

• tenant_id (UUID, FK to tenants.id, NOT NULL)

• agent_id (UUID, NOT NULL)

• execution_id (VARCHAR(64), NOT NULL)

• start_time (TIMESTAMP, NOT NULL)

4. Evaluation Layer for Offline and Online Scoring

Tables:

• evaluations
• evaluation_runs
• evaluation_results

Schema:

• evaluations

• id (UUID, PK, DEFAULT gen_random_uuid())

• tenant_id (UUID, FK to tenants.id, NOT NULL)

• name (VARCHAR(100), NOT NULL)

• mode (ENUM: offline, online, NOT NULL)

• criteria (JSONB, NOT NULL)

5. Failure Clustering and Taxonomy Management

Tables:

• failure_clusters
• cluster_members
• cluster_feedback
• cluster_annotations

Schema:

• failure_clusters

• id (UUID, PK, DEFAULT gen_random_uuid())

• tenant_id (UUID, FK to tenants.id, NOT NULL)

• cluster_vector (VECTOR(1536), NOT NULL)

• canonical_label (VARCHAR(128), NOT NULL)

• (ENUM: active, merged, split, deleted, NOT NULL)

6. Governance Dashboard with Role-Based Views

Tables:

• users
• roles
• user_roles
• audit_logs

Schema:

• users

• id (UUID, PK, DEFAULT gen_random_uuid())

• tenant_id (UUID, FK to tenants.id, NOT NULL)

• email (VARCHAR(128), NOT NULL, UNIQUE)

• password_hash (VARCHAR(128), NOT NULL)

• (ENUM: active, suspended, deleted, NOT NULL)

7. Agent-Side SDK Customization and Configuration

Tables: (see tenant_configs, above)

8. Alerting Integration with Incident Management and Security Tools

Tables:

• alert_integrations
• alert_delivery_logs

Schema:

• alert_integrations

• id (UUID, PK, DEFAULT gen_random_uuid())

• tenant_id (UUID, FK to tenants.id, NOT NULL)

• integration_type (ENUM: webhook, siem, api, pagerduty, splunk, servicenow, NOT NULL)

• endpoint_url (VARCHAR(255), NOT NULL)

• (JSONB, NULL)

ER Diagram

erDiagram
 tenants ||--o{ tenant_configs : "has"
 tenants ||--o{ users : "has"
 tenants ||--o{ agent_traces : "has"
 tenants ||--o{ telemetry_events : "has"
 tenants ||--o{ detections : "has"
 tenants ||--o{ evaluations : "has"
 tenants ||--o{ failure_clusters : "has"
 tenants ||--o{ roles : "has"
 tenants ||--o{ alert_integrations : "has"
 users ||--o{ user_roles : "has"
 roles ||--o{ user_roles : "has"
 users ||--o{ cluster_feedback : "gives"
 users ||--o{ cluster_annotations : "writes"
 agent_traces ||--o{ trace_steps : "contains"
 agent_traces ||--o{ trace_metadata : "has"
 agent_traces ||--o{ cluster_members : "member"
 agent_traces ||--o{ evaluation_results : "has"
 telemetry_events ||--o{ detections : "generates"
 telemetry_events ||--o{ event_replay_queue : "for"
 detections ||--o{ detection_alerts : "triggers"
 detections ||--o{ detection_results : "has"
 detection_alerts ||--o{ alert_delivery_logs : "delivers"
 failure_clusters ||--o{ cluster_members : "groups"
 failure_clusters ||--o{ cluster_feedback : "receives"
 failure_clusters ||--o{ cluster_annotations : "annotated"
 evaluations ||--o{ evaluation_runs : "runs"
 evaluation_runs ||--o{ evaluation_results : "produces"
 alert_integrations ||--o{ alert_delivery_logs : "logs"
 users ||--o{ audit_logs : "creates"

Partitioning & Sharding:

• telemetry_events, agent_traces, and failure_clusters partitioned by tenant_id and time for scale and retention.
• All vector columns indexed with pgvector for fast similarity search.
• Regular backups (pg_dump, WAL archiving) and PITR enabled.

5. API DESIGN

API Principles

• All endpoints are under /api/v1/
• OpenAPI 3.1 contracts auto-generated (FastAPI)
• JWT/OAuth2 Bearer token auth required on all endpoints; RBAC enforced
• All endpoints rate-limited per tenant (default: 1000 req/min/tenant, configurable)
• All endpoints return standard error codes (400, 401, 403, 404, 409, 422, 500) with structured error payloads.

Endpoints by Feature

1. High-Volume Ingestion Pipeline

• POST /api/v1/telemetry/events

• Request: { agent_id: str, event_type: str, event_timestamp: str, payload: dict }

• Response: { id: str, status: "accepted" }

• Auth: Bearer, tenant-scoped

• Rate Limit: 10,000 req/min/tenant

• GET /api/v1/telemetry/events?agent_id=:agent_id&from=:ts&to=:ts

• Response:

2. Real-Time Detection Layer

• GET /api/v1/detections?status=:status&type=:type

• Response: [{ id, detection_type, score, threshold, detected_at, status }]

• Auth: Bearer

• POST /api/v1/detection/rules

• Request: { rule_name: str, rule_type: str, criteria: dict, threshold: float }

• Response: { id: str }

3. LLM Reasoning Trace Capture

• POST /api/v1/traces

• Request: { agent_id: str, execution_id: str, steps: [ { step_type: str, step_payload: dict } ], metadata: dict }

• Response: { id: str, status: "accepted" }

• Auth: Bearer

• GET /api/v1/traces/:trace_id

• Response: { id, agent_id, execution_id, status, steps: [...], metadata: {...} }

4. Evaluation Layer

• POST /api/v1/evaluations

• Request: { name: str, mode: "offline"|"online", criteria: dict, llm_judge_config?: dict, policy_check_config?: dict }

• Response: { id: str }

• Auth: Bearer, admin

• POST /api/v1/evaluations/:eval_id/run

• Request: { model_version: str }

5. Failure Clustering

• GET /api/v1/failure_clusters

• Response: [ { id, canonical_label, status, member_count } ]

• Auth: Bearer

• GET /api/v1/failure_clusters/:cluster_id

• Response: { id, canonical_label, status, members: [...], annotations: [...] }

• Auth: Bearer

6. Governance Dashboard (API Layer)

• GET /api/v1/tenants/me

• Response: { id, name, compliance_policy, retention_policy_days }

• Auth: Bearer

• GET /api/v1/users/me

• Response: { id, email, roles }

• Auth: Bearer

• GET /api/v1/audit_logs?resource_type=:type

7. Agent-Side SDK Configuration

• GET /api/v1/tenant_configs

• Response: { event_types, sampling_rate, policy_rules, semantic_thresholds }

• Auth: Bearer, admin

• PATCH /api/v1/tenant_configs

• Request: { event_types?: list, sampling_rate?: float, policy_rules?: dict, semantic_thresholds?: dict }

• Response: { status: "updated" }

8. Alerting Integration

• POST /api/v1/alert_integrations

• Request: { integration_type: str, endpoint_url: str, auth_config?: dict }

• Response: { id: str }

• Auth: Bearer, admin

• GET /api/v1/alert_integrations

• Response: [ { id, integration_type, endpoint_url, enabled } ]

9. User/Role Management

• POST /api/v1/users

• Request: { email: str, password: str, roles: [str] }

• Response: { id: str }

• Auth: Bearer, admin

• GET /api/v1/users

• Response: [ { id, email, roles, status } ]

• Auth: Bearer, admin

API Structure Diagram

flowchart TD
 A["Agent SDK"] --> B["/api/v1/telemetry/events (POST)"]
 B --> C["Ingestion Gateway"]
 C --> D["/api/v1/telemetry/events/replay (POST)"]
 C --> E["/api/v1/detections (GET)"]
 C --> F["/api/v1/traces (POST)"]
 F --> G["/api/v1/traces/:trace_id (GET)"]
 C --> H["/api/v1/evaluations (POST)"]
 H --> I["/api/v1/evaluations/:eval_id/run (POST)"]
 H --> J["/api/v1/evaluations/:eval_id/results (GET)"]
 C --> K["/api/v1/failure_clusters (GET)"]
 K --> L["/api/v1/failure_clusters/:cluster_id (GET)"]
 L --> M["/api/v1/failure_clusters/:cluster_id/merge (POST)"]
 L --> N["/api/v1/failure_clusters/:cluster_id/split (POST)"]
 L --> O["/api/v1/failure_clusters/:cluster_id/annotate (POST)"]
 L --> P["/api/v1/failure_clusters/:cluster_id/feedback (POST)"]
 Q["/api/v1/alert_integrations (POST)"] --> R["Alerting Service"]
 S["/api/v1/tenant_configs (PATCH)"] --> T["Tenant Config Service"]
 U["/api/v1/auth/login (POST)"] --> V["Auth Service"]

6. COMPONENT ARCHITECTURE

Component Design by Feature

1. High-Volume Ingestion Pipeline

• AgentSDKAdapter: Modular Python client for agent frameworks. Pushes events to Ingestion Gateway with per-tenant config.
• IngestionGatewayService: FastAPI service; authenticates SDK requests, validates payloads, enqueues to Kafka.
• EventBuffer (Kafka): Topic-partitioned by tenant for isolation and replay.
• ReplayManager: Service for replaying historical events from buffer based on admin requests.

2. Real-Time Detection Layer

• DetectionProcessor: Consumes telemetry from Kafka, runs detection checks (semantic drift, policy, anomaly), writes results to detections.
• DetectionRuleEngine: Evaluates rules (from detection_rules) per tenant/event.
• DetectionAlertDispatcher: Triggers alerts via Alerting Service or integrations.

3. LLM Reasoning Trace Capture

• TraceCaptureService: Receives trace submissions (from SDK or pipeline), stores in agent_traces/trace_steps, indexes vectors.
• TraceQueryService: Provides search/replay APIs, integrates with OpenSearch for fast trace search.

4. Evaluation Layer

• EvaluationOrchestrator: Manages offline/online eval jobs, triggers LLM/Rule evaluations.
• EvalWorker: Batch/stream workers running evaluations, persisting results.
• LLMJudgeAdapter: Integrates with OpenAI/Anthropic for LLM-as-judge scoring.

5. Failure Clustering

• ClusteringService: Embedding-based clustering of traces/incidents, updates failure_clusters.
• ClusterFeedbackManager: Handles user feedback/merge/split/annotation actions.

6. Governance Dashboard (API Layer)

• GovernanceAPI: Exposes metrics, audit logs, compliance status, role-based data to frontend/clients.
• AuditLogger: Captures all admin/user actions for compliance.

7. Agent-Side SDK Config

• TenantConfigService: Manages per-tenant configs, propagates to SDKs via API.

8. Alerting Integration

• AlertingService: Dispatches alerts to integrations, logs delivery status.
• IntegrationConnectors: Managed connectors for PagerDuty, Splunk, ServiceNow, generic webhooks.

9. Multi-Tenancy & Auth

• AuthService: JWT/OAuth2, RBAC, tenant scoping.
• TenantIsolationMiddleware: Enforces tenant context on all API/database operations.

10. Observability/Monitoring

• TelemetryExporter: Emits traces, metrics, and logs to OpenTelemetry Collector.
• HealthCheckService: Exposes health endpoints for all services.

Component Data Flows

flowchart TD
 SDK["AgentSDKAdapter"] --> IG["IngestionGatewayService"]
 IG --> EB["EventBuffer (Kafka)"]
 EB --> DP["DetectionProcessor"]
 EB --> TC["TraceCaptureService"]
 EB --> EO["EvaluationOrchestrator"]
 DP --> DA["DetectionAlertDispatcher"]
 DA --> AS["AlertingService"]
 AS --> IC["IntegrationConnectors"]
 TC --> TQ["TraceQueryService"]
 EO --> EW["EvalWorker"]
 EW --> LLM["LLMJudgeAdapter"]
 EW --> FC["ClusteringService"]
 FC --> CFM["ClusterFeedbackManager"]
 IG --> TM["TenantIsolationMiddleware"]
 IG --> AU["AuthService"]
 AllServices --> TE["TelemetryExporter"]

7. INTEGRATION ARCHITECTURE

Third-Party Integrations

1. OpenAI API Integration

• Component: LLMJudgeAdapter, DetectionProcessor
• Protocol: HTTPS REST (OpenAI API)
• Auth: API Key (per-tenant or per-instance, stored in Vault)
• Data: JSON payloads (prompts, traces)
• Error Handling: Retry on 429/5xx, fallback to queue, alert on persistent failure
• Notes: SaaS model uses direct API; on-prem may use proxy or manual LLM integration.

2. Anthropic API Integration

• Component: LLMJudgeAdapter, DetectionProcessor
• Protocol: HTTPS REST (Anthropic API)
• Auth: API Key (per-tenant or per-instance)
• Data: JSON
• Error Handling: As above

3. Incident Management/Security Platforms

• Component: IntegrationConnectors, AlertingService
• Protocol: Webhooks (HTTPS POST), SIEM (Syslog/TCP), APIs (REST)
• Auth: OAuth2, HMAC, or API Key (per integration)
• Data: JSON alert payloads (with context, trace links)
• Error Handling: Exponential backoff/retry, DLQ for failed deliveries, alert logs for auditing

8. SECURITY CONSIDERATIONS

• Authentication: JWT Bearer tokens for all APIs, OAuth2 support, per-tenant key rotation; password hashes (bcrypt/argon2).
• Authorization: RBAC enforced at API and data layers; tenant ID scoping.
• Data Isolation: All DB queries/partitions scoped to tenant_id; no cross-tenant access.
• Encryption: TLS 1.3 for all in-flight data, AES-256-at-rest (PostgreSQL, OpenSearch, S3/MinIO).
• Secrets: All integration/API keys stored in HashiCorp Vault, never in code/repos.
• Audit Logging: All config/role/alert actions logged per tenant.
• Compliance: Configurable retention, GDPR/CCPA support (data export/delete), audit trails.
• Threat Modeling: Regular pen-test, dependency scanning, DDoS protection at ingress, rate limiting.
• Monitoring: Real-time alerting on auth failures, abnormal API use, and anomaly in event patterns.

9. SCALABILITY PLAN

• Horizontal Scaling:
• All stateless services (Ingestion, Detection, Trace, Eval, Clustering) are horizontally scalable via container orchestration (K8s Phase 2+).
• Kafka partitions per tenant/agent for parallelism.
• PostgreSQL and OpenSearch sharded/partitioned by tenant and time.
• Load Balancing:
• API Gateway and service endpoints behind L7 load balancer (managed in SaaS, ingress in on-prem).
• Caching:
• Redis for recent events/detections, per-tenant config cache.
• Performance Optimization:
• Async I/O (FastAPI, aiokafka), batch DB writes, vector index maintenance.
• Auto-Scaling:
• Phase 2+: K8s HPA/VPA, broker and DB cluster scaling.
• Bottleneck Mitigation:
• Back-pressure on ingestion, circuit breakers on LLM API calls, DLQs for failed tasks.
• Observability:
• Prometheus, Grafana, OpenTelemetry for metrics and traces.

10. DEPLOYMENT STRATEGY

Infrastructure Setup

• SaaS: Managed cloud (AWS/GCP/Azure), managed Kafka, RDS/PostgreSQL, OpenSearch Service, S3; K8s for orchestration.
• On-Prem/Air-Gapped: Docker Compose/Kubernetes, MinIO for object storage, self-hosted Kafka, PostgreSQL, OpenSearch.
• CI/CD: GitHub Actions for build/test/deploy, Docker images, versioned API contracts.
• Secrets: HashiCorp Vault for secrets, env vars for service config.
• Monitoring: OpenTelemetry Collector agents on all services, Prometheus for metrics, Grafana dashboards.
• Disaster Recovery: DB backups (WAL, snapshots), Kafka topic replication, DR plans for S3/MinIO.

10a. PHASE 1 DEPLOYMENT — Minimum Viable Infrastructure

Platform: Railway (managed PaaS, supports Docker Compose, managed PostgreSQL, Kafka, OpenSearch, Redis, Vault)

Phase 1 Topology:

• Single Railway project, one Docker Compose stack:
• services: ingestion-gateway (FastAPI), detection, trace, eval, clustering, alerting, governance-api, auth, observability
• managed PostgreSQL 16 (with pgvector)
• managed OpenSearch 3.0
• managed Kafka 4.1
• managed Redis 7
• managed Vault (for secrets)

Infrastructure-as-Code:

• Dockerfile for each service
• docker-compose.yml with all services, networks, healthchecks
• .env file for secrets/config (never commit secrets; inject via Railway dashboard or Vault)

Environment Variables:

• DATABASE_URL (PostgreSQL)
• KAFKA_BROKER_URL
• OPENSEARCH_URL
• REDIS_URL
• VAULT_ADDR
• JWT_SECRET
• TENANT_ID (for on-prem/air-gapped)
• LLM_API_KEYS (for OpenAI/Anthropic, per-tenant or global)

Database Setup:

• One managed PostgreSQL 16 instance (enable pgvector)
• Run bootstrap SQL migrations (e.g., Alembic) on deploy

Deferred to Phase 2+:

• Kubernetes, load balancer, CDN, auto-scaling, multi-region, full monitoring stack, service mesh, WAF, dedicated caching cluster

Estimated Setup Time: 4–8 hours for a solo developer

Deployment Topology Diagram:

flowchart TD
 subgraph "Railway Managed"
 A1["IngestionGateway (FastAPI)"]
 A2["DetectionService"]
 A3["TraceService"]
 A4["EvalService"]
 A5["ClusteringService"]
 A6["AlertingService"]
 A7["GovernanceAPI"]
 A8["AuthService"]
 B1["PostgreSQL 16 (pgvector)"]
 B2["Kafka 4.1"]
 B3["OpenSearch 3.0"]
 B4["Redis 7"]
 B5["Vault 1.15"]
 A1-->|"DB"|B1
 A2-->|"DB"|B1
 A3-->|"DB"|B1
 A4-->|"DB"|B1
 A5-->|"DB"|B1
 A6-->|"DB"|B1
 A7-->|"DB"|B1
 A1-->|"Kafka"|B2
 A2-->|"Kafka"|B2
 A3-->|"Kafka"|B2
 A4-->|"Kafka"|B2
 A5-->|"Kafka"|B2
 A6-->|"Kafka"|B2
 A7-->|"Kafka"|B2
 A1-->|"Search"|B3
 A3-->|"Search"|B3
 A5-->|"Search"|B3
 A1-->|"Cache"|B4
 A2-->|"Cache"|B4
 A3-->|"Cache"|B4
 A4-->|"Cache"|B4
 A5-->|"Cache"|B4
 A6-->|"Cache"|B4
 A7-->|"Cache"|B4
 A1-->|"Secrets"|B5
 A2-->|"Secrets"|B5
 A3-->|"Secrets"|B5
 A4-->|"Secrets"|B5
 A5-->|"Secrets"|B5
 A6-->|"Secrets"|B5
 A7-->|"Secrets"|B5
 end

11. TRADE-OFFS

• Microservices vs. Monolith: Microservices add operational complexity but are required for scale, isolation, and decoupled real-time/batch processing.
• Kafka vs. Simpler Queues: Kafka is more complex to operate than Redis/RabbitMQ but is necessary for replay, partitioning, and high throughput.
• PostgreSQL + OpenSearch: Dual storage increases ops burden but enables both transactional and fast search workloads.
• Multi-tenancy: Adds complexity to every layer but is non-negotiable for enterprise compliance.
• LLM API Integration: Reliance on external APIs enables advanced detection/eval but introduces external dependencies, costs, and connectivity challenges for on-prem deployments.
• Phase 1 Simplicity vs. Phase 2 Scale: Compose-based single-node deployment is fast for v0, but full production requires K8s, autoscaling, and multi-region.

12. ALTERNATIVES CONSIDERED

• Monolithic FastAPI App:

• Simpler ops, but cannot scale ingestion and detection independently; not suitable for high-throughput, multi-tenant enterprise workloads.

• RabbitMQ/Redis for Messaging:

• Easier to manage, but lacks partitioning/replay required for enterprise event pipelines.

• Single DB (PostgreSQL only):

• Simpler, but search/analytics queries would degrade ingestion performance and limit fast vector search/clustering.

• Serverless (Lambda/Step Functions):

• Fast to prototype, but cold start/timeout limits and state management challenges for high-volume, low-latency ingestion.

When Alternatives Might Be Reconsidered:

• Monolith: For MVP/prototyping in very small teams.
• RabbitMQ/Redis: For on-prem with strict ops constraints and lower scale.
• Serverless: For non-critical, small-scale deployments with bursty but not sustained load.
• Single DB: For test/dev environments or very small tenants.

Validation Checklist:

• ✅ All features from Feature List are covered (with specific tables, endpoints, components)
• ✅ No generic placeholders remain; all schemas and APIs are fully explicit
• ✅ Database schema is complete, multi-tenant, and mapped to all features
• ✅ API endpoints are comprehensive (30+), mapped to features, with schemas and error handling
• ✅ Components are specific and named for each feature
• ✅ Technology choices and versions are derived from context and web evidence
• ✅ All Q&A requirements and deployment models are addressed
• ✅ Production readiness, security, and scalability are integral
• ✅ Phase 1 deployment is concrete, actionable, and solo-developer friendly

Product Requirements Document (PRD)

AI Agent Observability & Trust Platform

1. Executive Summary

The AI Agent Observability & Trust Platform is an enterprise-grade SaaS (and on-premise/air-gapped) API service designed to monitor, govern, and ensure the reliability and compliance of AI agents (LLM-based copilots, chatbots, and autonomous agents) in Fortune-500 environments. The platform ingests high-volume telemetry from diverse agent frameworks, detects behavioral drift and policy violations in near real-time, and builds a structured, searchable "failure memory" to prevent repeated incident resolution. Key differentiators include semantic drift detection, automated failure clustering, full LLM reasoning trace capture, and role-based governance dashboards tailored for CISOs, AI platform leaders, and engineering teams. The solution is purpose-built for regulated, high-risk industries, providing rapid deployment, strict multi-tenancy, and seamless integration with leading LLM APIs and enterprise security tools.

Key Objectives:

• Enable enterprises to deploy and monitor AI agents with confidence, ensuring compliance, reliability, and proactive risk management.
• Reduce manual investigation time and prevent repeated resolution of the same AI failures.
• Provide actionable, real-time alerts and forensic traceability for security and engineering stakeholders.

2. Problem Statement

As Fortune-500 enterprises increasingly deploy AI agents in production, they face acute challenges in monitoring, governing, and ensuring the reliability and compliance of these systems. Risks include behavioral drift, policy violations (e.g., data privacy, content compliance), and recurring, unpredictable failures that can lead to compliance breaches, security incidents, customer harm, and costly manual investigations. Existing workarounds—ad hoc logging, manual incident review, basic output sampling, and custom scripts—are fragmented, unscalable, and lack institutional memory. There is no unified, real-time, enterprise-grade observability and trust layer for AI agents that meets the needs of CISOs, AI leaders, and engineering teams, especially in regulated industries.

3. Solution Overview

The platform provides a unified API service for ingesting, processing, and analyzing telemetry from enterprise AI agents. It features:

• High-volume ingestion pipeline with agent-side SDKs and per-tenant isolation.
• Real-time semantic drift and policy violation detection using embeddings and configurable rules.
• Full LLM reasoning trace capture for forensic analysis and replay.
• Automated failure clustering and taxonomy using vector search, with user-in-the-loop feedback and annotations.
• Eval layer for offline regression and online continuous evaluation against historical failure memory.
• Role-based governance dashboards for CISOs (compliance, audit, risk) and engineering (execution traces, remediation).
• Seamless integration with OpenAI, Anthropic, PagerDuty, Splunk, ServiceNow, and more.
• Strict multi-tenancy and support for both SaaS and on-premise/air-gapped deployments.

This approach delivers real-time, semantic, and policy-aware observability, actionable institutional memory, and rapid deployment (within a day), tailored for the unique needs of enterprise AI.

4. Stakeholder Analysis

5. User Personas

Persona 1: CISO – "Alex Martinez"

• Demographics: Age 48, Chief Information Security Officer, US-based, Fortune-500 financial institution (50,000+ employees)
• Goals/Objectives: Ensure AI agent compliance with GDPR, CCPA, and FINRA; minimize regulatory and reputational risk; maintain audit trails.
• Pain Points: Lack of real-time visibility into AI agent behavior; fragmented compliance monitoring; high cost of manual incident review.
• Use Cases: Receive sub-second alerts for policy violations; review audit logs; enforce and update compliance rules; export compliance reports.
• Technology Comfort: High (familiar with SIEM, compliance dashboards, API integrations)
• Behavioral Patterns: Proactive in risk management, requires robust auditability, prefers automated, actionable alerts.

Persona 2: AI Platform Lead – "Priya Singh"

• Demographics: Age 36, Director of AI Platforms, UK-based, global healthcare provider (20,000+ employees)
• Goals/Objectives: Deploy and monitor LLM-based agents (OpenAI, Anthropic, LangChain); ensure reliability and rapid iteration; reduce downtime.
• Pain Points: Difficulty tracking agent drift and recurring failures; slow incident resolution; lack of institutional memory for agent errors.
• Use Cases: Configure agent-side SDKs; monitor real-time drift detection; analyze failure clusters; run offline regression tests before model updates.
• Technology Comfort: Very high (hands-on with Python, FastAPI, ML Ops, agent orchestration frameworks)
• Behavioral Patterns: Data-driven, iterative, collaborates with engineering and compliance, values rapid feedback.

Persona 3: Engineering Team Lead – "Jordan Lee"

• Demographics: Age 29, Senior ML Engineer, Singapore, multinational insurance company (10,000+ employees)
• Goals/Objectives: Integrate telemetry SDK into custom orchestrators; debug and remediate agent failures; optimize agent performance.
• Pain Points: Manual, repetitive incident diagnosis; lack of structured trace capture; difficulty correlating agent outputs with failures.
• Use Cases: Push structured events via SDK; search and replay LLM reasoning traces; provide feedback on failure clusters; annotate root causes.
• Technology Comfort: Expert (Python, Kafka, PostgreSQL, OpenSearch, Docker)
• Behavioral Patterns: Hands-on, prefers API-first solutions, values automation and extensibility.

Persona 4: Compliance Analyst – "Emily Chen"

• Demographics: Age 41, Compliance Analyst, US-based, Fortune-500 healthcare company
• Goals/Objectives: Validate adherence to HIPAA, GDPR, and internal policies; review and export audit logs; investigate policy violations.
• Pain Points: Insufficient auditability of AI agent decisions; manual data collation for compliance reports; slow response to violations.
• Use Cases: Access governance dashboard; review detection results; export audit logs; update policy rules.
• Technology Comfort: Moderate (comfortable with dashboards, basic API usage)
• Behavioral Patterns: Detail-oriented, process-driven, collaborates with security and engineering.

Persona 5: IT Operations Lead – "Michael O’Connor"

• Demographics: Age 52, IT Ops Lead, Ireland, global insurance provider (15,000+ employees)
• Goals/Objectives: Deploy and manage platform (SaaS/on-prem); ensure uptime and performance; manage integration with internal systems.
• Pain Points: Complexity of multi-tenant deployments; managing secrets and compliance in air-gapped environments; ensuring seamless updates.
• Use Cases: Deploy Docker Compose/K8s stack; monitor service health; manage secrets via Vault; configure alert integrations.
• Technology Comfort: High (DevOps, Docker, Kubernetes, Vault, cloud/on-prem hybrid)
• Behavioral Patterns: Reliability-focused, prefers automation, requires clear documentation and support.

6. Technical Requirements

Architecture Alignment

All technical requirements are strictly aligned with the approved Architecture v1.0.0 (see reference above).

Technology Stack

Component Architecture

• AgentSDKAdapter: Modular Python client for LangChain, custom orchestrators, etc.
• IngestionGatewayService: FastAPI, async, validates/authenticates, enqueues to Kafka.
• EventBuffer: Kafka, partitioned by tenant.
• DetectionProcessor: Async, runs semantic drift, policy, anomaly checks.
• DetectionRuleEngine: Per-tenant, flexible rule evaluation.
• TraceCaptureService: Stores traces in PostgreSQL/OpenSearch.
• EvaluationOrchestrator: Manages eval jobs, triggers LLM/Rule evals.
• ClusteringService: Embedding-based, uses pgvector.
• AlertingService: Webhooks, SIEM, PagerDuty, Splunk, ServiceNow.
• GovernanceAPI: Exposes metrics, audit logs, compliance.
• TenantConfigService: Manages per-tenant config, propagates to SDKs.
• AuthService: JWT/OAuth2, RBAC, tenant scoping.
• Observability: OpenTelemetry, Prometheus, Grafana.

Frontend

• Angular, modular, role-based dashboards (CISO, Engineering, Compliance).
• Consumes GovernanceAPI, TraceAPI, DetectionAPI.

Backend

• Python 3.14.6, FastAPI 0.140.0, async-first microservices.
• Kafka 4.1 for event streaming.
• PostgreSQL 16 with pgvector for transactional and vector search.
• OpenSearch 3.0 for fast, multi-tenant search.
• Redis 7 for caching.
• Celery 6 for background/batch jobs.

Infrastructure

• Docker Compose for local/dev/air-gapped deployments.
• Kubernetes 1.32 for scale (Phase 2+).
• Managed Railway PaaS for SaaS.
• HashiCorp Vault for secrets.
• S3/MinIO for object storage.

Security

• JWT/OAuth2 Bearer tokens, RBAC, per-tenant key rotation.
• TLS 1.3, AES-256-at-rest.
• Audit logging, DDoS protection, rate limiting.

Integration

• OpenAI/Anthropic APIs for LLM-based detection/eval.
• Webhooks, SIEM, PagerDuty, Splunk, ServiceNow for alerting.

Configuration & Performance

• All ingestion and detection services are horizontally scalable.
• Kafka partitions per tenant for isolation.
• Async I/O, batch DB writes, vector index maintenance.
• Redis for config/event cache.

7. Non-Functional Requirements

8. Success Metrics & KPIs

9. Risks

10. Assumptions

• All tenants will integrate via the provided agent-side SDK or compatible API.
• Tenants can configure telemetry event types, sampling, and policy rules via API or dashboard.
• SaaS model has continuous access to OpenAI/Anthropic APIs; on-prem may require local proxy/manual updates.
• All critical detection rules and compliance standards (GDPR, CCPA, HIPAA, FINRA) are supported out of the box.
• Engineering teams will provide feedback on failure clusters (validate, merge, split, annotate).
• All API endpoints and database schemas will remain backward compatible within a major version.
• On-premise deployments may have delayed access to new features/integrations due to update cadence.
• All secrets (API keys, tokens) are managed via Vault and not stored in code/config files.
• Each tenant operates in a strictly isolated data partition; no cross-tenant access is permitted.
• All external integrations (alerting, LLM APIs) are documented for SaaS/on-prem differences.

11. Compliance & Regulatory Requirements

• GDPR: Data export/delete APIs, configurable retention, audit logs.
• CCPA: Right to access/delete, opt-out support.
• HIPAA: Data handling, audit trails, access controls (for healthcare tenants).
• FINRA: Retention, auditability, policy enforcement (for finance tenants).
• Industry-Specific: Ability to define and enforce custom policies per tenant.
• Auditability: All actions logged, exportable per tenant.
• Data Residency: Support for regional deployment (SaaS), on-premise for strict residency.

12. Security & Privacy Requirements

• Authentication: JWT Bearer, OAuth2, per-tenant key rotation.
• Authorization: RBAC at API/data layers, strict tenant scoping.
• Data Isolation: All data partitioned by tenant at every layer.
• Encryption: TLS 1.3 in transit, AES-256-at-rest (PostgreSQL, OpenSearch, S3/MinIO).
• Secrets Management: HashiCorp Vault, no secrets in code/repos.
• Audit Logging: All admin/user/config/alert actions logged.
• Threat Modeling: Regular pen-testing, dependency scanning, DDoS protection, rate limiting.
• Monitoring: Real-time alerting on auth failures, abnormal API use, anomaly in event patterns.
• Privacy Controls: Configurable data retention, export/delete APIs, PII masking in logs.

13. Integration Requirements

• LLM APIs: OpenAI API (2026), Anthropic API (2026); per-tenant or global API keys.
• Incident/Security Tools: PagerDuty, Splunk, ServiceNow, SIEM (Syslog/TCP), webhooks, REST APIs.
• Alerting: JSON payloads, OAuth2/HMAC/API Key auth, exponential backoff/retry, DLQ for failed deliveries.
• SDK Integration: Modular adapters for LangChain, custom orchestrators.
• On-Premise: Local proxies/manual updates for cloud-dependent integrations.
• API Contracts: OpenAPI 3.1, auto-generated, codegen for SDKs.
• Monitoring/Observability: OpenTelemetry, Prometheus, Grafana.

14. Data Architecture

Database Schema (per Architecture)

• Transactional DB: PostgreSQL 16 with pgvector extension.
• Search: OpenSearch 3.0 for fast, multi-tenant search.
• Partitioning: All major tables partitioned by tenant_id and time.
• Tables: tenants, tenant_configs, telemetry_events, event_replay_queue, detections, detection_rules, detection_alerts, detection_results, agent_traces, trace_steps, , , , , , , , , , , , , , .

Example Table: telemetry_events

ER Diagram Reference

erDiagram
 tenants ||--o{ tenant_configs : "has"
 tenants ||--o{ users : "has"
 tenants ||--o{ agent_traces : "has"
 tenants ||--o{ telemetry_events : "has"
 tenants ||--o{ detections : "has"
 tenants ||--o{ evaluations : "has"
 tenants ||--o{ failure_clusters : "has"
 tenants ||--o{ roles : "has"
 tenants ||--o{ alert_integrations : "has"
 users ||--o{ user_roles : "has"
 roles ||--o{ user_roles : "has"
 users ||--o{ cluster_feedback : "gives"
 users ||--o{ cluster_annotations : "writes"
 agent_traces ||--o{ trace_steps : "contains"
 agent_traces ||--o{ trace_metadata : "has"
 agent_traces ||--o{ cluster_members : "member"
 agent_traces ||--o{ evaluation_results : "has"
 telemetry_events ||--o{ detections : "generates"
 telemetry_events ||--o{ event_replay_queue : "for"
 detections ||--o{ detection_alerts : "triggers"
 detections ||--o{ detection_results : "has"
 detection_alerts ||--o{ alert_delivery_logs : "delivers"
 failure_clusters ||--o{ cluster_members : "groups"
 failure_clusters ||--o{ cluster_feedback : "receives"
 failure_clusters ||--o{ cluster_annotations : "annotated"
 evaluations ||--o{ evaluation_runs : "runs"
 evaluation_runs ||--o{ evaluation_results : "produces"
 alert_integrations ||--o{ alert_delivery_logs : "logs"
 users ||--o{ audit_logs : "creates"

Storage Strategies

• Transactional Data: PostgreSQL 16, partitioned by tenant and time.
• Vector Search: pgvector extension for embeddings (drift/failure clustering).
• Search: OpenSearch 3.0 for trace/failure memory search.
• Object Storage: S3/MinIO for large artifacts (traces, logs).
• Cache: Redis 7 for recent events/configs.

Data Processing & Flow

• Ingestion: Agent SDK → Ingestion Gateway (FastAPI) → Kafka (partitioned).
• Detection: Kafka → DetectionProcessor (semantic drift, policy, anomaly).
• Trace Capture: Kafka → TraceCaptureService → PostgreSQL/OpenSearch.
• Clustering: Trace vectors → ClusteringService → Failure Clusters.
• Evaluation: EvaluationOrchestrator → EvalWorker → LLMJudgeAdapter/OpenAI/Anthropic.
• Alerting: DetectionProcessor → AlertingService → IntegrationConnectors.

15. API Specifications

All endpoints are under /api/v1/ and strictly match the Architecture.

Telemetry Ingestion

• POST /api/v1/telemetry/events

  • Request: { agent_id: str, event_type: str, event_timestamp: str, payload: dict }
  • Response: { id: str, status: "accepted" }
  • Auth: Bearer, tenant-scoped
  • Rate Limit: 10,000 req/min/tenant

• GET /api/v1/telemetry/events?agent_id=:agent_id&from=:ts&to=:ts

  • Response: [{ id, agent_id, event_type, event_timestamp, payload }]
  • Auth: Bearer

• POST /api/v1/telemetry/events/replay

  • Request: { event_ids: [str] }

Detection

• GET /api/v1/detections?status=:status&type=:type

  • Response: [{ id, detection_type, score, threshold, detected_at, status }]
  • Auth: Bearer

• POST /api/v1/detection/rules

  • Request: { rule_name: str, rule_type: str, criteria: dict, threshold: float }
  • Response: { id: str }
  • Auth: Bearer, admin

• GET /api/v1/detection/rules

  • Response: [{ id, rule_name, rule_type, criteria, threshold, enabled }]

Trace Capture & Search

• POST /api/v1/traces

  • Request: { agent_id: str, execution_id: str, steps: [ { step_type: str, step_payload: dict } ], metadata: dict }
  • Response: { id: str, status: "accepted" }
  • Auth: Bearer

• GET /api/v1/traces/:trace_id

  • Response: { id, agent_id, execution_id, status, steps: [...], metadata: {...} }
  • Auth: Bearer

• GET /api/v1/traces/search?query=:q

  • Response: [{ id, agent_id, execution_id, status, start_time }]

Evaluation

• POST /api/v1/evaluations

  • Request: { name: str, mode: "offline"|"online", criteria: dict, llm_judge_config?: dict, policy_check_config?: dict }
  • Response: { id: str }
  • Auth: Bearer, admin

• POST /api/v1/evaluations/:eval_id/run

  • Request: { model_version: str }
  • Response: { run_id: str, status: "started" }
  • Auth: Bearer, admin

• GET /api/v1/evaluations/:eval_id/runs

Failure Clustering

• GET /api/v1/failure_clusters

  • Response: [ { id, canonical_label, status, member_count } ]
  • Auth: Bearer

• GET /api/v1/failure_clusters/:cluster_id

  • Response: { id, canonical_label, status, members: [...], annotations: [...] }
  • Auth: Bearer

• POST /api/v1/failure_clusters/:cluster_id/merge

  • Request: { target_cluster_id: str }
  • Response: { status: "merged" }

Tenant & User Management

• GET /api/v1/tenants/me

  • Response: { id, name, compliance_policy, retention_policy_days }
  • Auth: Bearer

• GET /api/v1/users/me

  • Response: { id, email, roles }
  • Auth: Bearer

• GET /api/v1/audit_logs?resource_type=:type

  • Response: [ { id, action, resource_type, resource_id, timestamp } ]
  • Auth: Bearer, admin

Alerting & Integrations

• POST /api/v1/alert_integrations

  • Request: { integration_type: str, endpoint_url: str, auth_config?: dict }
  • Response: { id: str }
  • Auth: Bearer, admin

• GET /api/v1/alert_integrations

  • Response: [ { id, integration_type, endpoint_url, enabled } ]
  • Auth: Bearer

• PATCH /api/v1/alert_integrations/:integration_id

  • Request: { enabled?: bool, endpoint_url?: str, auth_config?: dict }

User Management & Auth

• POST /api/v1/users

  • Request: { email: str, password: str, roles: [str] }
  • Response: { id: str }
  • Auth: Bearer, admin

• GET /api/v1/users

  • Response: [ { id, email, roles, status } ]
  • Auth: Bearer, admin

• PATCH /api/v1/users/:user_id

  • Request: { status?: str, roles?: [str] }
  • Response: { status: "updated" }

All endpoints require JWT/OAuth2 Bearer tokens, are tenant-scoped, and enforce RBAC.

16. Testing Strategy

• Unit Testing: All core services (Ingestion, Detection, Trace, Eval, Clustering, Auth) with pytest.
• Integration Testing: End-to-end API tests (FastAPI TestClient), Kafka event flow, DB migrations.
• E2E Testing: Synthetic agent telemetry, full pipeline (ingest → detect → alert → cluster).
• Performance Testing: Load test ingestion (10,000+ events/min), detection latency, alert delivery.
• Security Testing: Pen-testing, JWT/OAuth2 validation, RBAC enforcement, secrets scanning.
• Regression Testing: Offline evals against historical failure memory.
• CI/CD: Automated via GitHub Actions, with coverage and static analysis gates.

17. Deployment Strategy

• Phase 1: Railway PaaS, Docker Compose stack (all services, managed PostgreSQL/Kafka/OpenSearch/Redis/Vault).
• On-Premise: Docker Compose or K8s, MinIO for object storage, self-hosted Kafka/PostgreSQL/OpenSearch.
• CI/CD: GitHub Actions, Docker images, versioned OpenAPI contracts.
• Rollback: Docker Compose/K8s rollback, DB migration versioning, backup restore.
• Blue-Green Deployment: Planned for Phase 2+ (K8s).
• Secrets: Vault-injected env vars, never in code.
• Monitoring: OpenTelemetry agents, Prometheus, Grafana dashboards.
• Disaster Recovery: DB backups (WAL, snapshots), Kafka topic replication, S3/MinIO DR plans.

18. Monitoring & Observability

• Metrics: Ingestion rate, detection latency, alert delivery success, cluster formation, API errors.
• Dashboards: Grafana (per-tenant and global), Prometheus metrics.
• Logging: Centralized, structured logs (OpenTelemetry).
• Tracing: Distributed traces for all microservices.
• Alerts: Automated on SLO/SLA breaches, auth failures, abnormal event patterns.
• Health Checks: All services expose /health endpoints.

19. Timeline & Phases

20. Resource Requirements

• Team Composition:
  • Product Owner (1)
  • Backend Engineers (2-3, Python/FastAPI/Kafka/Postgres)
  • Frontend Engineer (1, Angular)
  • DevOps/Infra (1, Docker/K8s/Vault)
  • Security/Compliance (0.5 FTE, shared)
  • QA/Testing (1)
• Skills Needed: Python, FastAPI, Kafka, PostgreSQL, OpenSearch, Redis, Docker, Vault, OAuth2/JWT, Angular, CI/CD, Prometheus, Grafana.
• Budget Considerations: Cloud infra (managed Kafka, Postgres, OpenSearch), LLM API usage, on-prem support, professional services for integration/customization.

21. Change Management Plan

• User Adoption: Detailed onboarding docs, codegen SDKs, sample integrations for LangChain/custom orchestrators.
• Training: Role-based training for CISOs, engineering, compliance, and IT Ops.
• Communication: Release notes, changelogs, in-app notifications for new features/policy updates.
• Feedback Loop: In-app feedback, user-in-the-loop clustering, regular user interviews.
• Support: Premium support SLAs for enterprise tenants, dedicated onboarding for design partners.

End of PRD

User Stories & Epics

Epic: Secure and Authenticated API Access

This epic covers providing secure authentication and authorization mechanisms for all API endpoints, SDKs, and integrations to protect tenant data and platform integrity, including role-based access control and secure secrets management.

Implement Role-Based Access Control (RBAC) Enforcement

As the system, I want to enforce RBAC at API and data layers, so that users and services have access only to authorized resources within their tenant scope.

Priority: high

Dependencies: Implement JWT and OAuth2 Authentication for APIs and SDKs (37ae5097-ac24-4534-8d8b-c323d39eafd1)

Acceptance Criteria:

• Functional: API endpoints enforce RBAC permissions per user role.
• Functional: Data queries are filtered by tenant and role permissions.
• Technical: RBAC policies are configurable and stored securely.
• Technical: Unauthorized access attempts return 403 errors.
• Technical: RBAC enforcement is tested with unit and integration tests.

Story Points: 8

Estimated Effort: 8 hours

Implement JWT and OAuth2 Authentication for APIs and SDKs

As the system, I want to authenticate all API and SDK requests using JWT Bearer tokens and OAuth2 flows, so that only authorized users and services can access platform resources.

Priority: high

Acceptance Criteria:

• Functional: All API endpoints require valid JWT or OAuth2 tokens.
• Functional: Supports token refresh and revocation.
• Technical: Tokens are tenant-scoped and include RBAC claims.
• Technical: Authentication service integrates with Vault for key rotation.
• Technical: Authentication failures are logged and monitored.

Story Points: 8

Estimated Effort: 8 hours

Epic: Data Retention and Compliance Controls

This epic covers implementing configurable data retention policies and compliance controls per tenant to meet enterprise regulatory requirements such as GDPR, CCPA, HIPAA, and FINRA.

Implement Automated Data Deletion and Export for Compliance

As the system, I want to automatically delete or export tenant data according to retention policies and compliance requests, so that regulatory requirements like GDPR and CCPA are met.

Priority: high

Dependencies: Implement Tenant APIs for Data Retention Configuration (3a796e08-9a77-4884-8941-b5832a0982a9)

Acceptance Criteria:

• Functional: Data older than retention period is deleted securely.
• Functional: Supports data export and deletion requests per tenant.
• Technical: Deletion jobs run asynchronously with audit logging.
• Technical: Data export supports standard formats (JSON, CSV).
• Technical: Compliance actions are logged and auditable.

Story Points: 8

Estimated Effort: 8 hours

Implement Tenant APIs for Data Retention Configuration

As a tenant admin, I want APIs to specify data retention durations and deletion policies, so that platform usage complies with legal and regulatory obligations.

Priority: high

Dependencies: Implement Configurable Data Retention and Compliance Policies (1ea10a17-3f8b-4bf8-ac20-ac6d66d63f08)

Acceptance Criteria:

• Functional: API supports setting retention policies with validation.
• Functional: Policies can be updated and versioned.
• Technical: API enforces RBAC and tenant scoping.
• Technical: Changes are logged in audit logs.
• Technical: API responses conform to OpenAPI 3.1.

Story Points: 5

Estimated Effort: 5 hours

Epic: Scalability for Enterprise-Scale Telemetry

This epic covers ensuring the platform can scale horizontally to handle high-volume telemetry ingestion, processing, storage, and querying for large enterprise tenants while maintaining performance under peak loads.

Optimize Storage and Query Performance for Large Data Volumes

As the platform, I want to optimize database and search engine performance for large telemetry and trace data volumes, so that queries remain performant at scale.

Priority: high

Dependencies: Implement Trace Indexing and Search API (38b5afff-c3aa-4a82-bdee-b815a12a1422), Implement Embedding-Based Failure Clustering Service (240dba66-daf2-40f4-8744-620f0a605707)

Acceptance Criteria:

• Functional: Queries on telemetry events, traces, and clusters perform within SLA.
• Technical: Database schemas are partitioned and indexed appropriately.
• Technical: OpenSearch indices are optimized for multi-tenant search.
• Technical: Caching layers reduce load on primary databases.
• Technical: Performance tests validate query latency under load.

Story Points: 8

Estimated Effort: 8 hours

Implement Horizontal Scaling for Ingestion and Processing Services

As the platform, I want ingestion and processing services to scale horizontally, so that the system can handle 10,000+ concurrent agents per tenant without degradation.

Priority: high

Dependencies: Implement Kafka-Based Event Buffer with Tenant Partitioning (665e5481-0184-48ad-a0b6-1feb018c5607), Implement Async Microservices for Detection, Trace, Evaluation, and Clustering (74545937-1014-43f9-a5ff-7ff049d89d7f)

Acceptance Criteria:

• Functional: Services support multiple instances behind load balancers.
• Functional: Kafka partitions scale with tenant and agent volume.
• Technical: Services are stateless and support auto-scaling.
• Technical: Monitoring tracks scaling metrics and triggers alerts.
• Technical: Load tests validate performance under peak loads.

Story Points: 8

Estimated Effort: 8 hours

Epic: Configurable Policy Violation Detection Rules

This epic covers supporting highly flexible policy violation detection rules that allow customers to define custom rules, adjust thresholds, and extend semantic/contextual checks via APIs or user interfaces, enabling tailored compliance monitoring.

Enable Tenant API for Creating and Modifying Policy Violation Rules

As a tenant admin, I want to create, modify, and extend policy violation detection rules via API, so that I can customize compliance monitoring to evolving regulations and internal policies.

Priority: high

Dependencies: Develop APIs for Managing Detection Rules (db361ded-1f1b-4c02-99e7-a681bef98703)

Acceptance Criteria:

• Functional: API supports CRUD operations on policy violation rules.
• Functional: Rules support custom criteria and threshold adjustments.
• Technical: API enforces RBAC and tenant scoping.
• Technical: Changes propagate to detection processor dynamically.
• Technical: Validation ensures rule correctness and security.

Story Points: 5

Estimated Effort: 5 hours

Epic: Low-Latency Alerting

This epic covers ensuring that critical behavioral drift and policy violation alerts are generated and delivered with sub-second to low-second latency to enable proactive incident response, while batch checks operate on longer intervals for trend analysis.

Implement Batch Processing for Non-Critical Detection Checks

As the detection system, I want to run batch detection checks on longer intervals (minutes to hours) for trend analysis, so that comprehensive evaluation is possible without impacting real-time performance.

Priority: high

Dependencies: Implement Behavioral Anomaly Scoring with Tiered Latency Targets (03282f1e-057f-4b62-8d49-e0e2a0968dc6)

Acceptance Criteria:

• Functional: Batch detection jobs run on configurable schedules.
• Functional: Batch results are stored and accessible via API.
• Technical: Uses Celery for scheduled batch processing.
• Technical: Batch jobs do not impact real-time detection throughput.
• Technical: Batch job failures are logged and retried.

Story Points: 5

Estimated Effort: 5 hours

Implement Sub-Second Latency Alert Generation and Delivery

As the detection and alerting system, I want to generate and deliver critical alerts within sub-second to low-second latency, so that customers can respond proactively to AI agent issues.

Priority: high

Dependencies: Implement Behavioral Anomaly Scoring with Tiered Latency Targets (03282f1e-057f-4b62-8d49-e0e2a0968dc6), Implement Alert Delivery via Webhooks, SIEM, and APIs (c982f1d3-9ef3-4ebf-9a1d-b402338e0fa2)

Acceptance Criteria:

• Functional: Critical alerts are generated within 1 second of detection.
• Functional: Alert delivery latency to integrations is under 2 seconds.
• Technical: Uses in-memory caching and async event processing.
• Technical: Monitors latency metrics and triggers alerts on SLA breach.
• Technical: Supports fallback and retry mechanisms for delivery.

Story Points: 8

Estimated Effort: 8 hours

Epic: Event-Driven Microservices Architecture with Message Broker

This epic covers the implementation of a microservices architecture with an event-driven design using a message broker to separate fast ingestion paths from heavier asynchronous processing. It ensures scalability and fault tolerance of ingestion and processing pipelines.

Implement Async Microservices for Detection, Trace, Evaluation, and Clustering

As the platform, I want to implement microservices for detection, trace capture, evaluation, and failure clustering that consume events asynchronously from Kafka, so that processing is scalable and decoupled.

Priority: high

Dependencies: Implement Kafka-Based Event Buffer with Tenant Partitioning (665e5481-0184-48ad-a0b6-1feb018c5607)

Acceptance Criteria:

• Functional: Each microservice consumes relevant Kafka topics asynchronously.
• Functional: Services process events independently and update databases.
• Technical: Services use FastAPI with async I/O and Celery for batch jobs.
• Technical: Services emit OpenTelemetry traces and Prometheus metrics.
• Technical: Services implement health checks and auto-restart on failure.

Story Points: 13

Estimated Effort: 13 hours

Implement Kafka-Based Event Buffer with Tenant Partitioning

As the ingestion gateway, I want to enqueue telemetry events to a Kafka message broker partitioned by tenant, so that ingestion is scalable, isolated, and supports replay.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b)

Acceptance Criteria:

• Functional: Kafka topics are partitioned by tenant ID.
• Functional: Events are durable and ordered per tenant partition.
• Technical: Uses aiokafka client with async producers and consumers.
• Technical: Supports replay by re-consuming events from offsets.
• Technical: Monitors Kafka health and partitions for fault tolerance.

Story Points: 8

Estimated Effort: 8 hours

Epic: Support for SaaS and On-Premise/Air-Gapped Deployment Models

This epic covers deployment flexibility to support fully managed SaaS with continuous updates and integrations, as well as on-premise or air-gapped deployments with limited or manual integration updates and stricter data flow controls.

Implement On-Premise/Air-Gapped Deployment with Local Proxies and Manual Updates

As the platform maintainer, I want to support on-premise and air-gapped deployments with local proxies for external integrations and manual update mechanisms, so that customers with strict data flow controls can use the platform securely.

Priority: high

Dependencies: Design SaaS Deployment with Managed Integrations and Rapid Onboarding (db913a78-4926-4c88-b7d1-838957041e6c)

Acceptance Criteria:

• Functional: On-prem deployment supports core features with limited or delayed external API calls.
• Functional: Local proxies handle OpenAI/Anthropic API calls securely.
• Technical: Manual update process for integration connectors and platform components.
• Technical: Strict data flow controls and network isolation are enforced.
• Technical: Documentation details feature availability differences and trade-offs.

Story Points: 8

Estimated Effort: 8 hours

Design SaaS Deployment with Managed Integrations and Rapid Onboarding

As the platform maintainer, I want to provide a SaaS deployment model with managed integrations and rapid onboarding, so that customers can quickly deploy and scale the platform.

Priority: high

Acceptance Criteria:

• Functional: SaaS deployment supports all core features and integrations.
• Functional: Continuous updates and monitoring are enabled.
• Technical: Uses managed Kafka, PostgreSQL, OpenSearch, and Vault services.
• Technical: Supports multi-AZ for availability and disaster recovery.
• Technical: Provides onboarding automation and documentation.

Story Points: 8

Estimated Effort: 8 hours

Epic: Multi-Tenant Architecture with Data Isolation

This epic covers the implementation of strict per-tenant data isolation and retention policies to meet enterprise compliance and security requirements. It ensures logical separation and access control of tenant data across all system layers.

Implement Configurable Data Retention and Compliance Policies

As a tenant admin, I want to specify data retention durations and deletion policies, so that platform usage aligns with legal and regulatory obligations.

Priority: high

Dependencies: Implement Tenant Data Partitioning and Access Control (4dd60acc-5193-4d8b-80d4-b0774bf05d33)

Acceptance Criteria:

• Functional: API supports setting retention policies per tenant.
• Functional: Retention policies are enforced automatically by background jobs.
• Technical: Data older than retention period is deleted or archived securely.
• Technical: Compliance with GDPR, CCPA, HIPAA, and FINRA is ensured.
• Technical: Retention changes are logged and auditable.

Story Points: 8

Estimated Effort: 8 hours

Implement Tenant Data Partitioning and Access Control

As the system, I want to enforce tenant data partitioning and access control at database and API layers, so that tenant data privacy and compliance are guaranteed.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b)

Acceptance Criteria:

• Functional: All database queries are scoped by tenant ID.
• Functional: API endpoints enforce tenant scoping and RBAC.
• Technical: Database schemas support partitioning by tenant and time.
• Technical: No cross-tenant data leakage is possible.
• Technical: Audit logs record data access per tenant.

Story Points: 8

Estimated Effort: 8 hours

Epic: Incident Management and Security Platform Integrations

This epic covers integration with enterprise incident management and security tools such as PagerDuty, Splunk, and ServiceNow. It supports alert delivery using standard protocols and configurable routing per tenant.

Develop APIs for Configuring Incident Management Integrations

As a tenant admin, I want APIs to configure which incident management tools to integrate with and how alerts are routed, so that alerting fits organizational processes.

Priority: high

Dependencies: Implement Integration Connectors for PagerDuty, Splunk, and ServiceNow (9bab17cb-f56e-4c6a-a966-d54cea7f89bd)

Acceptance Criteria:

• Functional: API supports CRUD operations on alert integrations with platform-specific configs.
• Functional: Allows enabling/disabling integrations and setting routing preferences.
• Technical: API enforces RBAC and tenant scoping.
• Technical: Changes propagate to integration connectors promptly.
• Technical: API responses conform to OpenAPI 3.1.

Story Points: 5

Estimated Effort: 5 hours

Implement Integration Connectors for PagerDuty, Splunk, and ServiceNow

As the integration connector, I want to deliver alerts to PagerDuty, Splunk, and ServiceNow using their APIs and protocols, so that alerts are integrated into customer workflows.

Priority: high

Dependencies: Implement Alert Delivery via Webhooks, SIEM, and APIs (c982f1d3-9ef3-4ebf-9a1d-b402338e0fa2)

Acceptance Criteria:

• Functional: Supports alert delivery to PagerDuty via REST API.
• Functional: Supports alert delivery to Splunk via SIEM connectors.
• Functional: Supports alert delivery to ServiceNow via REST API.
• Technical: Authentication uses OAuth2, API keys, or HMAC as required.
• Technical: Implements retry and dead-letter queue for failed deliveries.

Story Points: 8

Estimated Effort: 8 hours

Epic: Anthropic API Integration

This epic covers integration with the Anthropic API to support AI/LLM layer functionalities including semantic drift detection, evaluation, and reasoning trace capture. It includes authentication management and error handling.

Integrate Anthropic API for Embeddings and Trace Enrichment

As the LLMJudgeAdapter, I want to call Anthropic API for embeddings, evaluation, and trace enrichment, so that the platform provides complementary AI capabilities.

Priority: high

Dependencies: Implement Offline Evaluation Framework with Historical Failure Memory (5d292973-057d-4626-b9ce-cdcedd3ed7ae), Implement Embedding-Based Semantic Drift Detection (72840e4e-e601-42e8-b1eb-ad9e85d0a506)

Acceptance Criteria:

• Functional: Supports API calls to Anthropic for embeddings and evaluation.
• Technical: Manages API authentication securely using Vault-stored keys.
• Technical: Handles API rate limits with retry and backoff.
• Technical: Logs API usage and errors.
• Technical: Supports fallback mechanisms on failures.

Story Points: 5

Estimated Effort: 5 hours

Epic: OpenAI API Integration

This epic covers integration with the OpenAI API to support AI/LLM layer functionalities such as semantic drift detection, evaluation scoring, and reasoning trace enrichment. It includes handling authentication, rate limits, and error handling.

Integrate OpenAI API for Embedding Generation and Evaluation

As the LLMJudgeAdapter, I want to call OpenAI API to generate embeddings and perform evaluation scoring, so that the platform leverages advanced AI capabilities for semantic analysis.

Priority: high

Dependencies: Implement Offline Evaluation Framework with Historical Failure Memory (5d292973-057d-4626-b9ce-cdcedd3ed7ae), Implement Embedding-Based Semantic Drift Detection (72840e4e-e601-42e8-b1eb-ad9e85d0a506)

Acceptance Criteria:

• Functional: Supports API calls to OpenAI for embeddings and evaluation.
• Technical: Manages API authentication securely using Vault-stored keys.
• Technical: Handles rate limiting with retries and exponential backoff.
• Technical: Logs API usage and errors for monitoring.
• Technical: Supports fallback to queue on persistent failures.

Story Points: 5

Estimated Effort: 5 hours

Epic: Alerting Integration with Incident Management and Security Tools

This epic covers integration of alerting mechanisms with customers’ existing incident management and security platforms via standard protocols. It supports configurable alert routing, role-based notifications, and secure, reliable delivery to enable proactive incident response.

Develop APIs for Managing Alert Integrations and Routing

As a tenant admin, I want APIs to configure alert integrations, enable/disable endpoints, and set routing preferences per tenant and user role, so that alerting aligns with organizational workflows.

Priority: high

Dependencies: Implement Alert Delivery via Webhooks, SIEM, and APIs (c982f1d3-9ef3-4ebf-9a1d-b402338e0fa2)

Acceptance Criteria:

• Functional: API supports CRUD operations on alert integrations with validation.
• Functional: Supports enabling/disabling integrations and updating auth configs.
• Functional: Allows setting alert routing and notification preferences.
• Technical: API enforces RBAC and tenant scoping.
• Technical: Changes propagate to alerting service in near real-time.

Story Points: 5

Estimated Effort: 5 hours

Implement Alert Delivery via Webhooks, SIEM, and APIs

As the alerting service, I want to deliver alerts to external incident management and security platforms using webhooks, SIEM connectors, and APIs, so that alerts integrate seamlessly into enterprise workflows.

Priority: high

Dependencies: Build Alerting Mechanisms Integrated with Incident Management Tools (d6aa9452-5767-4d55-8541-cb25c40dcfe4)

Acceptance Criteria:

• Functional: Supports alert delivery to PagerDuty, Splunk, ServiceNow, and generic webhooks.
• Functional: Alert payloads include context and trace links.
• Technical: Supports OAuth2, HMAC, and API key authentication for integrations.
• Technical: Implements exponential backoff, retry, and dead-letter queue for failed deliveries.
• Technical: Logs alert delivery status and response codes.

Story Points: 8

Estimated Effort: 8 hours

Epic: Agent-Side SDK Customization and Configuration

This epic covers tenant control over telemetry data collection via the SDK, including configuration of event types, sampling rates, and policy violation detection rules or semantic thresholds. It ensures observability is tailored to compliance and operational needs without disrupting agent workflows.

Enable Definition and Extension of Policy Violation Detection Rules via SDK Config

As a tenant admin, I want to define and extend policy violation detection rules and semantic thresholds via SDK configuration, so that detection is tailored to evolving compliance requirements.

Priority: high

Dependencies: Expose Tenant Configuration APIs for Telemetry Event Types and Sampling (1d28e93c-1db7-4655-b6d0-7a70f6a0ad36)

Acceptance Criteria:

• Functional: SDK fetches updated policy rules and semantic thresholds from tenant config API.
• Functional: SDK applies updated rules dynamically without disrupting agent workflows.
• Technical: Policy rules are JSON schemas with validation.
• Technical: SDK caches rules and refreshes on config change events.
• Technical: SDK logs configuration application status for troubleshooting.

Story Points: 5

Estimated Effort: 5 hours

Expose Tenant Configuration APIs for Telemetry Event Types and Sampling

As a tenant admin, I want APIs to configure which telemetry event types to capture and adjust sampling rates, so that I can balance observability with performance and compliance.

Priority: high

Dependencies: Implement Tenant-Specific Telemetry Configuration APIs in SDK (67259a43-c948-4905-8525-e11bab7a4fe7)

Acceptance Criteria:

• Functional: API allows getting and patching tenant telemetry configuration.
• Functional: Configuration changes propagate to SDKs without agent restarts.
• Technical: Configuration stored in tenant_configs table with versioning.
• Technical: API enforces RBAC and tenant scoping.
• Technical: Changes trigger cache invalidation and SDK refresh.

Story Points: 5

Estimated Effort: 5 hours

Epic: Governance Dashboard with Role-Based Views

This epic covers the development of role-based governance dashboards tailored for CISOs/security leaders and engineering teams. It provides views for policy compliance, audit trails, risk perimeter, execution trace exploration, regression alignment, and remediation tracking.

Develop Dashboard Views for Engineering Teams

As an engineering team member, I want dashboard views showing execution traces, regression test results, and remediation status, so that I can investigate and resolve AI agent issues efficiently.

Priority: medium

Dependencies: Implement Role-Based Access Control for Governance API (051f3a23-007e-4b08-bd97-2c5a79ce3b8b), Implement Trace Indexing and Search API (38b5afff-c3aa-4a82-bdee-b815a12a1422), Implement Embedding-Based Failure Clustering Service (240dba66-daf2-40f4-8744-620f0a605707)

Acceptance Criteria:

• Functional: Dashboard provides trace exploration and failure cluster insights.
• Functional: Regression test results and remediation tracking are visible.
• Technical: UI supports search, filtering, and drill-down into trace details.
• Technical: Data is fetched securely with RBAC and tenant scoping.
• Technical: Dashboard is responsive and accessible.

Story Points: 8

Estimated Effort: 8 hours

Develop Dashboard Views for CISOs and Security Leaders

As a CISO, I want dashboard views showing policy compliance status, audit trails, and risk perimeter metrics, so that I can monitor organizational AI agent governance effectively.

Priority: medium

Dependencies: Implement Role-Based Access Control for Governance API (051f3a23-007e-4b08-bd97-2c5a79ce3b8b)

Acceptance Criteria:

• Functional: Dashboard displays compliance metrics and audit logs relevant to security leaders.
• Functional: Data is updated in near real-time with alert summaries.
• Technical: Dashboard consumes governance API with RBAC enforcement.
• Technical: UI supports filtering by tenant and time range.
• Technical: Performance optimized for large data volumes.

Story Points: 8

Estimated Effort: 8 hours

Implement Role-Based Access Control for Governance API

As the system, I want to enforce role-based access control on governance API endpoints, so that users see data relevant to their roles and permissions.

Priority: medium

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b)

Acceptance Criteria:

• Functional: API endpoints return data filtered by user roles (CISO, engineer, auditor).
• Functional: Unauthorized access is denied with appropriate error codes.
• Technical: RBAC is enforced at API and data query layers.
• Technical: Tenant scoping is strictly applied.
• Technical: Audit logs record access attempts and changes.

Story Points: 5

Estimated Effort: 5 hours

Epic: Failure Clustering and Taxonomy Management

This epic covers the automatic grouping of non-deterministic production incidents into structured canonical failure modes using embedding similarity and pattern discovery. It supports user-in-the-loop feedback, manual overrides, annotations, and maintains a searchable failure memory per tenant.

Develop User-In-The-Loop Feedback APIs for Failure Clusters

As an engineering team member, I want to validate, merge, split, and annotate failure clusters via APIs, so that failure taxonomies evolve with organizational knowledge.

Priority: high

Dependencies: Implement Embedding-Based Failure Clustering Service (240dba66-daf2-40f4-8744-620f0a605707)

Acceptance Criteria:

• Functional: APIs support cluster validation, merging, splitting, and annotation.
• Functional: Feedback and annotations are persisted and linked to user IDs.
• Technical: APIs enforce RBAC and tenant scoping.
• Technical: Cluster metadata updates propagate to clustering service.
• Technical: Audit logs capture feedback actions.

Story Points: 8

Estimated Effort: 8 hours

Implement Embedding-Based Failure Clustering Service

As the clustering service, I want to automatically group recurring AI failures into canonical failure modes using embedding similarity and pattern discovery, so that engineering teams get a structured failure knowledge base.

Priority: high

Dependencies: Implement Trace Indexing and Search API (38b5afff-c3aa-4a82-bdee-b815a12a1422)

Acceptance Criteria:

• Functional: Clustering service processes trace embeddings and groups similar failures.
• Functional: Failure clusters are stored with canonical labels and metadata.
• Technical: Uses pgvector and OpenSearch for vector similarity and search.
• Technical: Clustering runs asynchronously with incremental updates.
• Technical: Supports multi-tenant isolation and indexing.

Story Points: 8

Estimated Effort: 8 hours

Epic: Evaluation Layer for Offline and Online Scoring

This epic covers the evaluation framework that supports offline regression testing of new AI model versions against historical failure memory and continuous online scoring of live agent outputs for policy compliance and quality. It integrates LLM-as-judge and rule-based policy checks with configurable criteria per tenant.

Develop APIs for Managing Evaluation Jobs and Results

As a tenant admin, I want APIs to create, run, and query evaluation jobs and results, so that I can manage evaluation workflows and monitor outcomes.

Priority: high

Dependencies: Implement Offline Evaluation Framework with Historical Failure Memory (5d292973-057d-4626-b9ce-cdcedd3ed7ae)

Acceptance Criteria:

• Functional: API supports creating evaluation definitions with criteria and mode.
• Functional: API supports triggering evaluation runs with model version.
• Functional: API supports querying evaluation runs and results with filters.
• Technical: API enforces RBAC and tenant scoping.
• Technical: API responses conform to OpenAPI 3.1 contracts.

Story Points: 5

Estimated Effort: 5 hours

Implement Online Continuous Scoring of Live Agent Outputs

As the system, I want to continuously score live production agent outputs for policy compliance and quality baselines, so that ongoing compliance is monitored in real-time.

Priority: high

Dependencies: Implement Offline Evaluation Framework with Historical Failure Memory (5d292973-057d-4626-b9ce-cdcedd3ed7ae), Implement Embedding-Based Semantic Drift Detection (72840e4e-e601-42e8-b1eb-ad9e85d0a506)

Acceptance Criteria:

• Functional: Online evaluation runs streaming scoring of live telemetry events.
• Functional: Scores are updated in near real-time and available via API.
• Technical: Supports configurable evaluation criteria per tenant.
• Technical: Integrates with detection and alerting services for violations.
• Technical: Evaluation results are stored with timestamps and model versions.

Story Points: 8

Estimated Effort: 8 hours

Implement Offline Evaluation Framework with Historical Failure Memory

As an engineering team member, I want to run offline regression tests of new AI model versions against historical failure memory, so that I can validate model quality before deployment.

Priority: high

Dependencies: Implement Embedding-Based Failure Clustering Service (240dba66-daf2-40f4-8744-620f0a605707), Implement Trace Capture API and Storage (8aa13535-ea85-4533-b219-3ef970a4a2da)

Acceptance Criteria:

• Functional: Supports defining offline evaluation jobs with criteria and model version.
• Functional: Runs batch evaluation against stored failure clusters and traces.
• Technical: Evaluation results are stored and queryable via API.
• Technical: Supports LLM-as-judge and rule-based policy checks.
• Technical: Evaluation jobs run asynchronously with status tracking.

Story Points: 8

Estimated Effort: 8 hours

Epic: LLM Reasoning Trace Capture

This epic covers capturing and storing full execution traces of AI agent runs, including tool call sequences, intermediate reasoning steps, and contextual metadata. It supports multi-tenant isolation, indexing, and efficient retrieval for forensic analysis and replay.

Support Multi-Tenant Isolation for Trace Data

As the system, I want to ensure strict multi-tenant isolation of trace data at storage and API layers, so that tenant data privacy and compliance are maintained.

Priority: high

Dependencies: Implement Trace Capture API and Storage (8aa13535-ea85-4533-b219-3ef970a4a2da)

Acceptance Criteria:

• Functional: Trace data queries are scoped by tenant ID.
• Functional: No cross-tenant trace data leakage is possible.
• Technical: Database schemas enforce tenant partitioning and indexing.
• Technical: API layer enforces RBAC and tenant scoping.
• Technical: Audit logs record all trace data accesses.

Story Points: 5

Estimated Effort: 5 hours

Implement Trace Indexing and Search API

As a maintainer, I want to index trace data with vector embeddings and provide search APIs, so that users can efficiently retrieve and replay execution traces.

Priority: high

Dependencies: Implement Trace Capture API and Storage (8aa13535-ea85-4533-b219-3ef970a4a2da)

Acceptance Criteria:

• Functional: Traces are indexed with vector embeddings using pgvector and OpenSearch.
• Functional: Search API supports queries by text, metadata, and similarity.
• Technical: Search API enforces tenant isolation and RBAC.
• Technical: Search results include trace summaries and links to full details.
• Technical: Indexing pipeline processes new traces asynchronously.

Story Points: 8

Estimated Effort: 8 hours

Implement Trace Capture API and Storage

As a library consumer, I want to submit detailed reasoning traces including steps and metadata via API, so that full execution context of AI agent runs is captured for analysis.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b)

Acceptance Criteria:

• Functional: API accepts trace submissions with agent ID, execution ID, steps, and metadata.
• Functional: Trace data is stored in PostgreSQL with tenant isolation.
• Technical: Trace steps and metadata are stored in normalized tables with indexing.
• Technical: API validates payload schema and enforces rate limits.
• Technical: API requires JWT authentication and tenant scoping.

Story Points: 8

Estimated Effort: 8 hours

Epic: Real-Time Detection Layer

This epic covers the implementation of a detection system that performs semantic drift detection, policy violation checks, and behavioral anomaly scoring with tiered latency targets. It includes configurable detection rules, alerting mechanisms, and integration with incident management tools to enable proactive monitoring of AI agent behavior.

Develop APIs for Managing Detection Rules

As a tenant integrator, I want APIs to create, read, update, and disable detection rules, so that I can tailor policy violation detection to evolving compliance needs.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b)

Acceptance Criteria:

• Functional: API supports CRUD operations on detection rules with validation.
• Functional: Only authorized admin users can modify detection rules.
• Technical: API endpoints enforce RBAC and tenant scoping.
• Technical: Changes propagate to detection processor in near real-time.
• Technical: API responses conform to OpenAPI 3.1 contracts.

Story Points: 5

Estimated Effort: 5 hours

Build Alerting Mechanisms Integrated with Incident Management Tools

As the alerting service, I want to deliver detection alerts via webhooks, SIEM connectors, and APIs integrated with customer incident management platforms, so that alerts reach the right teams promptly.

Priority: high

Dependencies: Implement Behavioral Anomaly Scoring with Tiered Latency Targets (03282f1e-057f-4b62-8d49-e0e2a0968dc6)

Acceptance Criteria:

• Functional: Supports alert delivery via webhooks, SIEM, PagerDuty, Splunk, ServiceNow.
• Functional: Allows configuration of alert routing and notification preferences per tenant and role.
• Technical: Alert delivery supports retries, exponential backoff, and dead-letter queues.
• Technical: Alerts include contextual metadata and trace links for rapid investigation.
• Technical: Secure authentication and authorization for integration endpoints.

Story Points: 8

Estimated Effort: 8 hours

Implement Behavioral Anomaly Scoring with Tiered Latency Targets

As the detection processor, I want to score behavioral anomalies with tiered latency targets, providing sub-second alerts for critical issues and batch processing for less urgent checks, so that alerting is timely and scalable.

Priority: high

Dependencies: Implement Embedding-Based Semantic Drift Detection (72840e4e-e601-42e8-b1eb-ad9e85d0a506), Develop Policy Violation Detection Engine with Configurable Rules (89b1a2ee-c1cc-4044-9ac2-d81bd5d526b2)

Acceptance Criteria:

• Functional: Critical anomalies generate alerts within sub-second latency.
• Functional: Batch anomaly scoring runs on configurable intervals (minutes to hours).
• Technical: Detection processor supports real-time and batch processing modes.
• Technical: Uses Redis or in-memory cache for low-latency scoring.
• Technical: Alerting service receives and processes detection alerts promptly.

Story Points: 8

Estimated Effort: 8 hours

Develop Policy Violation Detection Engine with Configurable Rules

As the detection processor, I want to detect policy violations including data privacy, content compliance, and security policies using configurable rules per tenant, so that compliance issues are identified in real-time.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b), Implement Per-Tenant Data Isolation and Retention Policies in Ingestion Pipeline (86893a21-1b1e-4ac6-826d-c7be40dc3d8a)

Acceptance Criteria:

• Functional: Supports detection of GDPR, CCPA, HIPAA, FINRA, and custom policy violations.
• Functional: Detection rules are configurable via API and stored in detection_rules table.
• Technical: Rule engine evaluates JSON criteria against telemetry event payloads.
• Technical: Supports enabling/disabling rules and threshold adjustments.
• Technical: Detection results are persisted with detailed violation info.

Story Points: 8

Estimated Effort: 8 hours

Implement Embedding-Based Semantic Drift Detection

As the detection processor, I want to perform embedding-based semantic drift detection comparing live telemetry against baseline agent behavior per tenant, so that behavioral deviations are detected early.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b), Implement Per-Tenant Data Isolation and Retention Policies in Ingestion Pipeline (86893a21-1b1e-4ac6-826d-c7be40dc3d8a)

Acceptance Criteria:

• Functional: Detection identifies semantic drift events with scores and timestamps.
• Functional: Baseline behavior profiles are tenant-specific and configurable.
• Technical: Uses vector similarity search with pgvector in PostgreSQL.
• Technical: Supports asynchronous processing of telemetry events from Kafka.
• Technical: Detection results are stored in the detections table with status.

Story Points: 8

Estimated Effort: 8 hours

Epic: High-Volume Ingestion Pipeline with Agent-Side SDK

This epic covers the design and implementation of a scalable ingestion pipeline that allows agent-side SDKs to push structured telemetry events at enterprise scale. It includes SDK modularity, tenant-specific configuration, data isolation, and replay capabilities to support reliable telemetry ingestion without disrupting existing AI agent workflows.

Develop Event Replay Capability for Historical Telemetry Reprocessing

As a maintainer, I want to support replaying historical telemetry events on demand, so that downstream processing can reprocess events for updated detection or evaluation logic.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b), Implement Per-Tenant Data Isolation and Retention Policies in Ingestion Pipeline (86893a21-1b1e-4ac6-826d-c7be40dc3d8a)

Acceptance Criteria:

• Functional: Admin API allows submitting event IDs for replay.
• Functional: Replay manager reads events from storage and re-enqueues to Kafka.
• Technical: Replay preserves event ordering and data integrity.
• Technical: Replay jobs support status tracking (pending, processing, completed, failed).
• Technical: Replay respects tenant isolation and access controls.

Story Points: 5

Estimated Effort: 5 hours

Implement Per-Tenant Data Isolation and Retention Policies in Ingestion Pipeline

As the ingestion pipeline, I want to enforce strict per-tenant data isolation and configurable retention policies, so that tenant data privacy and compliance requirements are met.

Priority: high

Dependencies: Build High-Throughput Ingestion Gateway Service (29b7d171-303b-423f-8e61-2db646a7f49b)

Acceptance Criteria:

• Functional: Data stored is logically partitioned by tenant ID at all pipeline stages.
• Functional: Retention policies per tenant are configurable and enforced automatically.
• Technical: Kafka partitions and database schemas support tenant isolation.
• Technical: Retention enforcement triggers data deletion or archival per tenant policy.
• Technical: Access control prevents cross-tenant data leakage.

Story Points: 8

Estimated Effort: 8 hours

Build High-Throughput Ingestion Gateway Service

As the ingestion gateway service, I want to accept telemetry events from SDKs asynchronously, authenticate tenants, validate payloads, and enqueue events to a Kafka message broker with per-tenant partitioning, so that high-volume telemetry is ingested reliably and isolated per tenant.

Priority: high

Dependencies: Develop Modular Agent-Side SDK with Framework Adapters (79865b34-da6c-4fbd-8b91-d0c743499707)

Acceptance Criteria:

• Functional: API accepts telemetry events with required fields and returns acceptance status.
• Functional: Tenant authentication and authorization enforced using JWT tokens.
• Technical: Events are enqueued to Kafka topics partitioned by tenant ID.
• Technical: Supports async processing with backpressure and retry on transient failures.
• Technical: Validates event schema and rejects malformed payloads with appropriate error codes.

Story Points: 8

Estimated Effort: 8 hours

Implement Tenant-Specific Telemetry Configuration APIs in SDK

As a tenant integrator, I want to configure telemetry data collection via SDK APIs to select event types, adjust sampling rates, and define policy rules, so that telemetry observability aligns with compliance and operational needs.

Priority: high

Dependencies: Develop Modular Agent-Side SDK with Framework Adapters (79865b34-da6c-4fbd-8b91-d0c743499707)

Acceptance Criteria:

• Functional: SDK exposes APIs to retrieve and apply tenant-specific event type filters and sampling rates.
• Functional: SDK allows dynamic updates to configuration without restarting agent workflows.
• Technical: Configuration changes propagate via secure API calls or config files.
• Technical: SDK caches configuration locally with TTL and refresh logic.
• Technical: Configuration API supports JSON schema validation.

Story Points: 5

Estimated Effort: 5 hours

Develop Modular Agent-Side SDK with Framework Adapters

As a library consumer (developer), I want a modular agent-side SDK with adapters for diverse AI agent frameworks like LangChain and custom orchestrators, so that I can integrate telemetry event pushing seamlessly without disrupting existing workflows.

Priority: high

Acceptance Criteria:

• Functional: SDK supports multiple adapter modules for LangChain and custom orchestrators.
• Functional: SDK exposes flexible APIs for pushing structured telemetry events.
• Technical: SDK is implemented in Python with async support.
• Technical: SDK supports configuration loading for event types and sampling rates.
• Technical: SDK modules can be extended or replaced without recompilation.

Story Points: 8

Estimated Effort: 8 hours

Spike Stories (standalone)

Spike: Documentation Framework and Initial API Docs Setup

Spike

As a technical writer, I want to set up documentation frameworks and generate initial API docs using OpenAPI and Swagger UI, so that developers have access to up-to-date API specifications.

Priority: medium

Timebox: 3 days

Expected Outcomes:

• OpenAPI contracts generated from FastAPI services.
• Swagger UI hosted and accessible.
• Documentation site scaffolded.

Acceptance Criteria:

• API docs reflect current endpoints and schemas.
• Documentation is versioned and updated automatically.
• Developers can access docs locally and remotely.

Story Points: 3

Spike: Code Quality Tools Setup (ESLint, Prettier, Static Analysis)

Spike

As a developer, I want to set up code quality tools like ESLint and Prettier for frontend and static analysis tools for backend, so that code consistency and quality are maintained.

Priority: medium

Timebox: 2 days

Expected Outcomes:

• Linting and formatting tools configured.
• Pre-commit hooks enforce code style.
• Static analysis integrated into CI.

Acceptance Criteria:

• Code style violations are detected and reported.
• Auto-formatting works in developer IDEs.
• CI fails on code quality issues.

Story Points: 2

Spike: Testing Framework Setup for Unit, Integration, and E2E Tests

Spike

As a QA engineer, I need to set up testing frameworks (pytest for backend, Jest/Vitest for frontend), so that automated tests can be written and executed efficiently.

Priority: high

Timebox: 3 days

Expected Outcomes:

• Testing frameworks installed and configured.
• Sample unit and integration tests implemented.
• Test coverage reports generated.

Acceptance Criteria:

• Tests run successfully in local and CI environments.
• Coverage thresholds are defined.
• Test failures block merges.

Story Points: 3

Spike: Database Migration Framework Setup

Spike

As a backend engineer, I need to set up a database migration framework (e.g., Alembic) for PostgreSQL, so that schema changes can be versioned and applied consistently.

Priority: high

Timebox: 3 days

Expected Outcomes:

• Alembic configured with initial migrations.
• Migration scripts for core tables.
• Migration process documented.

Acceptance Criteria:

• Migrations apply cleanly on local and staging databases.
• Rollback scripts are tested.
• Migration commands integrated into CI/CD.

Story Points: 3

Spike: CI/CD Pipeline Setup with GitHub Actions

Spike

As a DevOps engineer, I need to configure CI/CD pipelines using GitHub Actions for build, test, and deployment automation, so that code changes are validated and deployed reliably.

Priority: high

Timebox: 5 days

Expected Outcomes:

• Automated build and test workflows.
• Docker image build and push to registry.
• Deployment automation to Railway or Kubernetes.

Acceptance Criteria:

• Pull requests trigger CI workflows.
• Failed tests block merges.
• Successful builds trigger deployments to staging.

Story Points: 5

Spike: Development Environment Setup with Docker and Local Dev Tools

Spike

As a developer, I need to set up a local development environment using Docker Compose and necessary dev tools, so that I can develop and test services locally.

Priority: high

Timebox: 3 days

Expected Outcomes:

• Docker Compose files for all core services.
• Local environment setup scripts and documentation.
• Verified local startup and health checks.

Acceptance Criteria:

• All services start locally with Docker Compose.
• Health endpoints respond successfully.
• Developers can run and debug services locally.

Story Points: 3

Spike: Repository Initialization and Project Scaffolding

Spike

As a developer, I need to initialize the project repository and scaffold the initial project structure, so that development can start with a clean, organized codebase.

Priority: high

Timebox: 3 days

Expected Outcomes:

• Initialized Git repository with standard branching strategy.
• Project scaffolding for backend and frontend services.
• Basic README and contribution guidelines.

Acceptance Criteria:

• Repository is accessible and cloned successfully.
• Project structure follows agreed architecture.
• Documentation covers setup instructions.

Story Points: 3

Engineering Tasks

Execution order should follow dependencies; complete "Depends on" tasks before each task.

Set up static analysis tools for backend Python code

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Integrate static analysis tools like mypy, bandit, and flake8 into backend services for type checking, security scanning, and code style enforcement.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Static analysis tools run without errors on clean code
• Issues are reported for violations
• Tools integrated into local dev and CI

Definition of Done:

• Configs and scripts committed
• CI pipeline runs static analysis
• Documentation updated with usage instructions

Initialize Git repository and configure remote origin

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Create a new Git repository with initial commit, configure remote origin on GitHub or preferred platform, and ensure repository is accessible and cloneable by the team.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Repository is created and accessible via remote URL
• Team members can clone repository successfully
• Initial commit contains basic README and .gitignore

Definition of Done:

• Git repository initialized and pushed to remote
• README with basic project info added
• Repository access verified by at least two team members

Create and test rollback migration scripts

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 0f868508-4e96-42bd-908b-dc0f1d071dd1
• Critical path: No
• Description: Develop rollback scripts for initial migrations and test applying and reverting migrations on local and staging databases to ensure safe schema changes.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.875h (1.6x factor)
• AI tools: Cursor
• AI category: testing (high confidence)

Acceptance Criteria:

• Rollback scripts revert schema changes cleanly
• No data loss or corruption during rollback tests
• Rollback tested on local and staging environments

Definition of Done:

• Rollback procedures documented
• Rollback tests automated in CI
• Test results reviewed and approved

Design and implement modular SDK core with async event pushing API

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop the core SDK module in Python with async support that exposes flexible APIs for pushing structured telemetry events. Ensure the core supports loading configuration for event types and sampling rates.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• SDK core exposes async APIs for telemetry event pushing
• Configuration loading for event types and sampling rates is supported
• SDK core is implemented in Python with async/await syntax

Definition of Done:

• Code implemented and reviewed
• Unit tests cover core SDK APIs
• Documentation updated with SDK core usage

Design and implement per-tenant data partitioning in Kafka and PostgreSQL

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Ensure Kafka topics and PostgreSQL tables are partitioned by tenant_id to enforce strict data isolation and support scalable ingestion and querying.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Kafka topics partitioned by tenant_id with proper keying
• PostgreSQL tables partitioned by tenant_id and event_timestamp
• Data isolation verified by tenant-scoped queries and access control

Definition of Done:

• Partitioning implemented and tested
• Integration tests verify tenant isolation
• Documentation updated with partitioning design

Develop policy violation detection engine with configurable rules

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement detection processor module that evaluates JSON-based policy rules per tenant against telemetry event payloads, supports enabling/disabling rules, threshold adjustments, and persists detailed violation results.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 3h (1.3x factor)
• AI tools: Claude Code (Anthropic)
• AI category: complex_algorithm (medium confidence)

Acceptance Criteria:

• Supports detection of GDPR, CCPA, HIPAA, FINRA, and custom policies
• Rule engine evaluates JSON criteria against event payloads accurately
• Detection results persisted with detailed violation info and status

Definition of Done:

• Policy detection engine implemented and tested
• Unit tests cover rule evaluation and persistence
• API or config integration for rule management

Integrate migration commands into CI/CD pipeline

• Type: devops | Priority: high | Status: todo
• Depends on (complete these first): 0f868508-4e96-42bd-908b-dc0f1d071dd1
• Critical path: No
• Description: Add Alembic migration apply and rollback commands to CI workflows to ensure schema changes are applied and tested automatically during build and deployment.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2.5h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• CI pipeline runs migrations on test/staging DBs
• Failed migrations block deployment
• Rollback tested in CI environment

Definition of Done:

• Migration steps added to CI workflows
• CI logs show successful migration runs
• Documentation updated with CI migration steps

Scaffold initial project directory structure following architecture guidelines

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 2172ecd6-17c5-42e9-8dac-1f555195f672
• Critical path: No
• Description: Create the base folder structure for backend microservices, shared libraries, configuration files, and documentation folders according to the approved architecture and tech stack.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.5h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Project structure matches architecture documentation
• Folders for services, migrations, tests, and docs are created
• Base config files (e.g., pyproject.toml, requirements.txt) are present

Definition of Done:

• Directory structure committed to repository
• Structure reviewed and approved by lead developer
• Documentation folder contains initial setup instructions

Configure CI to fail builds on code quality violations

• Type: devops | Priority: medium | Status: todo
• Depends on (complete these first): fb464dab-cf46-472a-b80d-558a660c2f00, cc26a7ab-bee1-4763-aca4-9be547ab8a6c, a1faf49e-fb14-4769-8b1d-089f7416d91f
• Critical path: No
• Description: Add steps in CI pipeline to run ESLint, Prettier check, mypy, bandit, and flake8, and fail builds if violations are detected.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2.5h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• CI pipeline runs all quality checks
• Builds fail on any code quality issues
• Reports are accessible in CI logs

Definition of Done:

• CI config updated and committed
• Pipeline tested with intentional violations
• Documentation updated with quality gate policy

Implement deployment workflow to staging environment

• Type: devops | Priority: high | Status: todo
• Depends on (complete these first): a1faf49e-fb14-4769-8b1d-089f7416d91f
• Critical path: No
• Description: Configure GitHub Actions to deploy successful builds to staging environment using Docker Compose or Kubernetes manifests.
• Estimate: 8 hours
• Story Points: 3
• Traditional estimate: 8h | AI-accelerated: 6.4h (1.25x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Successful builds trigger deployment to staging
• Deployment logs show no errors
• Staging environment services are healthy post-deployment

Definition of Done:

• Deployment workflow committed and tested
• Staging environment accessible and stable
• Rollback procedures documented

Write initial README with setup and contribution guidelines

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): e3a6d381-c7d6-4967-a5f3-36395356fa59
• Critical path: No
• Description: Document project overview, setup instructions, development workflow, coding standards, and contribution process in README.md to guide developers.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.5h (2x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• README includes clear setup steps for local development
• Coding standards and branch strategy are documented
• Contribution guidelines cover pull request and review process

Definition of Done:

• README.md committed and reviewed
• Documentation accessible from repository root
• Reviewed by product owner or tech lead

Implement automated API doc versioning and deployment

• Type: devops | Priority: medium | Status: todo
• Depends on (complete these first): a1faf49e-fb14-4769-8b1d-089f7416d91f, e7f64d84-eb40-4fb1-a598-63d65f943d4b
• Critical path: No
• Description: Set up CI/CD steps to generate and publish versioned API documentation artifacts on each release, accessible via web and downloadable formats.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 3h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• API docs generated and versioned on each build
• Docs published to accessible web location
• Documentation artifacts downloadable in JSON/YAML

Definition of Done:

• CI/CD workflows updated
• Docs accessible and tested
• Documentation updated with access instructions

Create SDK APIs for retrieving and applying tenant-specific telemetry config

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement SDK APIs to fetch tenant-specific telemetry configuration including event type filters, sampling rates, and policy rules from secure endpoints or config files.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (1.8x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• SDK can retrieve tenant config via secure API or config file
• Config includes event types, sampling rates, and policy rules
• Config changes can be applied dynamically without restart

Definition of Done:

• Config retrieval APIs implemented and tested
• Cache mechanism with TTL implemented
• Documentation updated with config API usage

Implement dynamic config update mechanism in SDK

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 2b1471fc-0a1f-4229-980d-48e99adb9958
• Critical path: No
• Description: Enable the SDK to detect and apply configuration changes dynamically at runtime without requiring agent workflow restarts.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• SDK detects config changes and refreshes local cache automatically
• Telemetry event filtering and sampling adjusts dynamically
• No agent workflow restarts required for config updates

Definition of Done:

• Dynamic config refresh implemented and tested
• Integration tests verify runtime config updates
• Documentation updated with dynamic config behavior

Implement offline evaluation job management and batch processing

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop APIs to create offline evaluation jobs with criteria and model version. Implement batch processing workers that run regression tests against historical failure clusters and traces. Store evaluation results in database and expose via API. Support LLM-as-judge and rule-based policy checks. Track job status asynchronously.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 5h (1.2x factor)
• AI tools: Claude Code (Anthropic)
• AI category: complex_algorithm (high confidence)

Acceptance Criteria:

• Offline evaluation jobs can be created with criteria and model version
• Batch processing runs regression tests and stores results
• Evaluation results queryable via API with tenant isolation

Definition of Done:

• Evaluation job APIs implemented and tested
• Batch processing workers developed and integrated
• Integration tests verify end-to-end evaluation flow

Set up pytest framework for backend unit and integration tests

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Configure pytest with necessary plugins, test discovery, and fixtures for backend Python services, ensuring tests can be run locally and in CI.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2.5h (1.6x factor)
• AI tools: Cursor
• AI category: testing (high confidence)

Acceptance Criteria:

• Pytest runs all tests and reports results
• Fixtures support database and Kafka mocking
• Tests run successfully in local and CI environments

Definition of Done:

• Pytest config files committed
• Sample tests created and passing
• Documentation updated with test running instructions

Design and implement PostgreSQL schema for trace storage

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Create normalized tables agent_traces, trace_steps, and trace_metadata with columns and constraints per architecture. Include tenant_id for multi-tenant isolation, indexes on tenant_id and agent_id, and pgvector column for trace_vector. Implement partitioning by tenant_id and time. Write Alembic migrations for schema setup.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.5h (2x factor)
• AI tools: Cursor
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Database schema matches architecture specifications with tenant isolation
• Indexes and partitioning implemented for performance and scale
• Alembic migrations created and tested for forward and rollback

Definition of Done:

• Database schema implemented and migrations tested
• Schema reviewed and approved by DBA or architect
• Integration tests verify data persistence and tenant isolation

Set up Jest and Vitest for frontend testing

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Configure Jest and Vitest testing frameworks for Angular frontend components, including unit and integration tests, with coverage reporting and CI integration.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2.5h (1.6x factor)
• AI tools: Cursor
• AI category: testing (high confidence)

Acceptance Criteria:

• Jest and Vitest run frontend tests successfully
• Coverage thresholds are defined and enforced
• Tests run in local and CI environments

Definition of Done:

• Test configs committed
• Sample frontend tests created and passing
• Documentation updated with test instructions

Develop docker-compose.yml for local multi-service orchestration

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 77f334c9-1412-4ec7-8d6c-4ddd8ced05f2
• Critical path: No
• Description: Create a Docker Compose file to orchestrate all backend services along with dependencies (PostgreSQL, Kafka, Redis, Vault, OpenSearch) for local development and testing.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 3h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• All services start successfully with docker-compose up
• Health endpoints respond correctly for each service
• Services communicate correctly over defined networks

Definition of Done:

• docker-compose.yml committed and tested
• Documentation includes instructions to start/stop services
• Network and volume configurations verified

Implement semantic drift detection processor consuming telemetry from Kafka

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop async detection processor service that consumes telemetry events from Kafka, computes embedding similarity against tenant baseline profiles using pgvector, and stores detection results in detections table.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 3h (1.3x factor)
• AI tools: Claude Code (Anthropic)
• AI category: complex_algorithm (medium confidence)

Acceptance Criteria:

• Processor consumes telemetry events asynchronously from Kafka
• Computes semantic drift scores using pgvector similarity search
• Detection results stored with status and timestamps in detections table

Definition of Done:

• Detection processor implemented and integrated
• Unit and integration tests cover detection logic and DB writes
• OpenAPI spec updated if applicable

Implement RBAC middleware enforcing role-based access control on detection APIs

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop middleware for FastAPI that enforces RBAC policies on detection-related endpoints, restricting access based on user roles and tenant scoping.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (1.8x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• RBAC middleware restricts detection rule modification to admin roles
• Tenant scoping enforced preventing cross-tenant access
• Unauthorized requests return 403 Forbidden

Definition of Done:

• RBAC middleware implemented and tested
• Integration tests verify access restrictions
• Documentation updated with RBAC policies

Implement Kafka producer integration with tenant partitioning in Ingestion Gateway

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): e330ee3a-d325-445f-966a-96e25f9973cb
• Critical path: No
• Description: Integrate aiokafka producer to enqueue validated telemetry events to Kafka topics partitioned by tenant ID, ensuring high throughput and isolation.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Events are enqueued to Kafka topic partitioned by tenant_id
• Kafka producer handles backpressure and retries on transient failures
• Telemetry events maintain ordering per tenant partition

Definition of Done:

• Kafka producer integrated and tested
• Integration tests verify event enqueue and ordering
• Monitoring metrics for producer success/failures added

Write unit and integration tests for trace capture API and storage

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 3f2ee986-5fc8-4774-96bb-ce21c2e103ae, 8e4a4607-56d7-4715-a9e0-acbd6f1dbe7b
• Critical path: No
• Description: Develop pytest unit tests for API validation, authentication, and error handling. Create integration tests to verify full trace submission flow, data persistence in PostgreSQL tables, tenant isolation, and rate limiting enforcement.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.25h (1.6x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover all validation and authentication scenarios
• Integration tests verify data stored correctly with tenant isolation
• Tests run successfully in CI with coverage thresholds met

Definition of Done:

• Tests implemented and passing locally and in CI
• Coverage reports generated and meet project standards
• Test code reviewed and merged

Configure local development tooling and debugging setup

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 77923311-08f5-4e0c-b045-d738d97447f0
• Critical path: No
• Description: Set up VSCode launch configurations, environment variable management, and logging to enable developers to run and debug services locally with breakpoints and live reload.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Developers can attach debugger to running containers
• Environment variables are loaded securely and correctly
• Logs are accessible and formatted for easy troubleshooting

Definition of Done:

• Launch.json and tasks.json committed
• Documentation updated with debugging instructions
• Tested by at least two developers

Define coverage thresholds and enforce in CI

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 108d341c-102c-4e2b-8d33-95ff23f62182, bdc41ad7-d29d-4164-92e8-2892a454e781, a1faf49e-fb14-4769-8b1d-089f7416d91f
• Critical path: No
• Description: Set minimum code coverage thresholds for backend and frontend tests and configure CI pipelines to fail builds if thresholds are not met.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.875h (1.6x factor)
• AI tools: Cursor
• AI category: testing (high confidence)

Acceptance Criteria:

• Coverage thresholds defined in config files
• CI pipeline fails on coverage below thresholds
• Coverage reports generated and accessible

Definition of Done:

• Coverage enforcement committed
• CI pipeline tested with failing coverage
• Documentation updated with coverage policy

Implement behavioral anomaly scoring with real-time and batch modes

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop detection processor functionality to score behavioral anomalies with tiered latency targets: sub-second alerts for critical anomalies and batch scoring for less urgent cases, using Redis cache for low-latency scoring.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 3h (1.3x factor)
• AI tools: Claude Code (Anthropic)
• AI category: complex_algorithm (medium confidence)

Acceptance Criteria:

• Critical anomalies generate alerts within sub-second latency
• Batch anomaly scoring runs on configurable intervals (minutes to hours)
• Alerting service receives and processes detection alerts promptly

Definition of Done:

• Anomaly scoring implemented and tested
• Performance tests verify latency targets
• Documentation updated with scoring modes

Integrate Alembic migration framework into backend services

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Add Alembic to backend services for managing PostgreSQL schema migrations, configure connection to PostgreSQL 16, and set up initial migration environment.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Alembic is installed and configured with correct DB URL
• Initial migration script created and applied successfully
• Migration commands work locally and in staging environment

Definition of Done:

• Alembic setup committed to repo
• Migration scripts versioned and tested
• Documentation updated with migration usage

Configure ESLint and Prettier for frontend Angular codebase

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Set up ESLint with Angular-specific rules and Prettier for code formatting, including integration with IDEs and pre-commit hooks.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.5h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• ESLint detects code style violations
• Prettier auto-formats code on save and commit
• Pre-commit hooks prevent commits with lint errors

Definition of Done:

• Lint and format configs committed
• IDE plugins configured and documented
• Pre-commit hooks tested and working

Implement retention policy enforcement for telemetry data per tenant

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 8bde14fd-a1fd-4866-969e-ad57498becf4
• Critical path: No
• Description: Develop background jobs or database policies to automatically delete or archive telemetry events based on tenant-configured retention durations.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Retention policies configurable per tenant and stored in tenant_configs
• Telemetry events older than retention period are deleted or archived
• Retention enforcement respects tenant isolation and does not affect other tenants

Definition of Done:

• Retention enforcement implemented and tested
• Integration tests verify data deletion per tenant
• Documentation updated with retention configuration and behavior

Set up OpenAPI and Swagger UI integration in FastAPI services

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Configure FastAPI to auto-generate OpenAPI 3.1 specs and serve Swagger UI documentation for all API endpoints under /api/v1/.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.5h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• OpenAPI specs generated automatically from code
• Swagger UI accessible at /docs or /api/v1/docs
• API docs include all endpoints and schemas

Definition of Done:

• Docs available on local and staging environments
• Documentation updated with usage instructions
• Reviewed by backend team

Develop REST APIs for failure cluster feedback and annotations

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement APIs to allow users to validate, merge, split, and annotate failure clusters. Persist feedback and annotations linked to user IDs and tenant IDs. Enforce RBAC and tenant scoping. Propagate metadata updates to clustering service asynchronously. Log all feedback actions in audit logs.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.33h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• APIs support cluster validation, merging, splitting, and annotation
• Feedback and annotations stored with user and tenant linkage
• RBAC and tenant isolation enforced on all endpoints

Definition of Done:

• APIs implemented with input validation and authentication
• Unit and integration tests cover all feedback operations
• Audit logging integrated and verified

Implement asynchronous indexing pipeline for trace embeddings

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop background worker service that consumes new agent_traces records, computes vector embeddings using OpenAI/Anthropic APIs, stores embeddings in PostgreSQL pgvector columns and indexes trace metadata in OpenSearch. Ensure multi-tenant isolation and incremental updates.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.67h (1.5x factor)
• AI tools: Claude Code (Anthropic)
• AI category: implementation (high confidence)

Acceptance Criteria:

• New traces are asynchronously indexed with vector embeddings
• Embeddings stored in PostgreSQL pgvector columns and OpenSearch
• Indexing respects tenant isolation and handles failures gracefully

Definition of Done:

• Indexing pipeline implemented and tested end-to-end
• Error handling and retries implemented for external API calls
• Integration tests verify indexing and search readiness

Document detection rule management APIs and usage

• Type: documentation | Priority: medium | Status: todo
• Depends on (complete these first): ec88b461-6f5c-44e4-873d-36f682f674b6
• Critical path: No
• Description: Write comprehensive API documentation for detection rule CRUD endpoints, including request/response schemas, authentication, RBAC, and examples.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 1h (1.8x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• API docs include all detection rule endpoints with examples
• Authentication and RBAC requirements clearly documented
• Docs integrated into OpenAPI and Swagger UI

Definition of Done:

• Documentation written and reviewed
• Docs accessible via Swagger UI
• Documentation versioned with API releases

Implement POST /api/v1/traces endpoint for trace capture

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop FastAPI endpoint POST /api/v1/traces to accept detailed reasoning traces including agent_id (UUID), execution_id (string), steps (array of step_type and step_payload), and metadata (key-value pairs). Validate request schema, enforce JWT authentication with tenant scoping, apply rate limiting, and return accepted status with trace ID.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.67h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• POST /api/v1/traces accepts valid payload with agent_id, execution_id, steps, and metadata
• API enforces JWT authentication and tenant scoping
• Rate limiting applied per tenant and endpoint

Definition of Done:

• Endpoint implemented with input validation and authentication
• Unit and integration tests cover endpoint behavior
• API documentation updated with request/response schema

Write integration tests for end-to-end telemetry ingestion and detection rule application

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): e330ee3a-d325-445f-966a-96e25f9973cb, ec88b461-6f5c-44e4-873d-36f682f674b6, 2502336e-d013-4850-838b-96d1d5fe3501, 39e52612-2c8a-4668-88e0-ef5b2b8a885d
• Critical path: No
• Description: Develop E2E tests simulating telemetry event ingestion through API, Kafka enqueue, detection processing with rules, and result persistence, verifying tenant isolation and RBAC enforcement.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (1.6x factor)
• AI tools: GitHub Copilot
• AI category: test_generation (high confidence)

Acceptance Criteria:

• E2E tests cover telemetry event ingestion to detection result
• Tenant isolation and RBAC enforced in all stages
• Tests run successfully in CI pipeline

Definition of Done:

• E2E tests implemented and passing
• Test coverage meets project standards
• Test documentation updated

Implement ReplayManager service to read and re-enqueue telemetry events

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 7a3bb596-06af-4400-ba64-de216aaa69e4
• Critical path: No
• Description: Develop a service that reads historical telemetry events by IDs from PostgreSQL and re-enqueues them to Kafka for downstream processing, preserving event order and tenant isolation.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• ReplayManager reads events by IDs from DB with tenant scoping
• Events are re-enqueued to Kafka maintaining original order
• Replay job status tracked with states: pending, processing, completed, failed

Definition of Done:

• ReplayManager implemented and tested
• Integration tests verify replay correctness and status updates
• Documentation updated with replay usage and API

Implement real-time propagation of detection rule changes to detection processor

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): ec88b461-6f5c-44e4-873d-36f682f674b6
• Critical path: No
• Description: Ensure that any changes to detection rules via API are propagated to the detection processor service in near real-time, using Kafka or Redis pub/sub for config invalidation.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (1.8x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Detection processor receives and applies rule changes within seconds
• Config invalidation uses Kafka or Redis pub/sub
• No downtime or restarts required for rule updates

Definition of Done:

• Real-time config propagation implemented and tested
• Integration tests verify detection processor updates
• Documentation updated with config propagation details

Enforce tenant isolation in trace data queries and API layer

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement middleware and query filters in trace-related API endpoints to scope all data access by tenant_id. Enforce RBAC policies restricting access to authorized roles and tenants. Add audit logging for all trace data accesses.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2.5h (1.2x factor)
• AI tools: Claude Code (Anthropic)
• AI category: security (medium confidence)

Acceptance Criteria:

• All trace data queries are filtered by tenant_id
• RBAC middleware restricts access based on user roles and tenant scope
• Audit logs record every trace data access with user and timestamp

Definition of Done:

• Tenant isolation enforced in all trace APIs
• RBAC middleware tested and integrated
• Audit logging verified with sample accesses

Write unit tests for RBAC middleware and detection API authorization

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): b245d527-aede-449a-86d7-1c35e8000acd
• Critical path: No
• Description: Create unit tests verifying RBAC middleware enforces role and tenant-based access control correctly on detection rule APIs.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 1h (1.6x factor)
• AI tools: GitHub Copilot
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Tests cover allowed and denied access scenarios
• Unauthorized access returns appropriate HTTP status
• Tests run successfully in CI

Definition of Done:

• Unit tests implemented and passing
• Coverage meets project standards
• Test documentation updated

Implement Real-Time Propagation of Alert Integration Changes to Alerting Service

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop mechanism to propagate alert integration configuration changes from API service to alerting service in near real-time using Kafka or Redis pub/sub to ensure alerting service uses latest configs.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: integration (high confidence)

Acceptance Criteria:

• Alert integration changes trigger config update events to alerting service
• Alerting service updates internal config cache within seconds
• No stale configs used for alert delivery

Definition of Done:

• Config propagation implemented and tested end-to-end
• Integration tests verify config update and alert delivery correctness
• Documentation updated for config propagation mechanism

Optimize Governance Dashboard Performance for Large Data Volumes

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Implement frontend and backend optimizations to ensure governance dashboard queries and UI rendering perform efficiently with large datasets, including pagination, caching, and query indexing.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Dashboard queries use pagination and filters to limit data volume
• Caching implemented to reduce redundant API calls
• UI renders smoothly with large datasets without lag

Definition of Done:

• Performance benchmarks meet defined thresholds
• Integration tests verify pagination and caching behavior
• Code reviewed and merged

Write tests for trace indexing and search APIs with tenant isolation

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): ed86bbf4-cf7d-4a82-8936-2a18f980690d, dde6b355-b610-43de-be63-74e0a48f5e32
• Critical path: No
• Description: Create unit and integration tests verifying trace indexing pipeline correctness, search API functionality, tenant scoping, and RBAC enforcement. Include tests for edge cases and error handling.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.625h (1.6x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Tests cover indexing pipeline success and failure scenarios
• Search API tests verify tenant isolation and RBAC
• Tests run successfully in CI with coverage reports

Definition of Done:

• Tests implemented and passing
• Coverage meets project standards
• Test code reviewed and merged

Develop Background Jobs for Automated Data Deletion per Retention Policy

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement Celery scheduled tasks that periodically delete or archive data older than tenant-configured retention periods from telemetry_events, agent_traces, and failure_clusters tables, ensuring compliance with GDPR, CCPA, HIPAA, and FINRA.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Background jobs run on configurable schedules
• Data older than retention period is securely deleted or archived
• Deletion operations are logged and auditable

Definition of Done:

• Background jobs implemented and scheduled
• Integration tests verify data deletion correctness
• Documentation includes retention policy enforcement details

Implement Kafka Producer with Tenant Partitioning in Ingestion Gateway

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop the Kafka producer integration in the ingestion gateway service using aiokafka client. Ensure telemetry events are published to Kafka topics partitioned by tenant_id, supporting high throughput and isolation.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Kafka topics are partitioned by tenant_id
• Telemetry events are published asynchronously with backpressure handling
• Producer handles retries and logs failures

Definition of Done:

• Kafka producer code implemented and tested
• Integration tests verify partitioning and throughput
• Documentation updated with Kafka usage

Develop GET /api/v1/traces/search API endpoint

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): ed86bbf4-cf7d-4a82-8936-2a18f980690d
• Critical path: No
• Description: Implement FastAPI GET /api/v1/traces/search endpoint supporting query parameters for text search, metadata filters, and vector similarity. Enforce JWT authentication, tenant scoping, and RBAC. Return paginated trace summaries with links to full trace details.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Search API supports text, metadata, and similarity queries
• Results are tenant-scoped and enforce RBAC
• Response includes trace summaries and links to detail endpoints

Definition of Done:

• Search API implemented and integrated with OpenSearch
• Unit and integration tests cover search scenarios
• API documentation updated with examples

Implement embedding-based failure clustering service

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop microservice that consumes trace embeddings, performs clustering using pgvector similarity and pattern discovery algorithms, stores failure clusters with canonical labels and metadata in PostgreSQL, and indexes clusters in OpenSearch. Ensure asynchronous incremental updates and multi-tenant isolation.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 5h (1.2x factor)
• AI tools: Claude Code (Anthropic)
• AI category: complex_algorithm (high confidence)

Acceptance Criteria:

• Clustering service groups similar failures based on embeddings
• Failure clusters stored with canonical labels and metadata
• Service supports multi-tenant isolation and incremental updates

Definition of Done:

• Clustering service implemented and integrated with event streams
• Unit and integration tests cover clustering logic and storage
• Performance benchmarks meet scalability targets

Enforce RBAC and Tenant Scoping in Governance Data Queries

• Type: development | Priority: medium | Status: todo
• Depends on (complete these first): 3b39227b-921e-4c1e-b6cc-b8e90959e528
• Critical path: No
• Description: Modify governance API data access layers to apply RBAC filters and tenant_id scoping in all database queries to ensure users only retrieve authorized data.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.7h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: security (high confidence)

Acceptance Criteria:

• All governance API queries include tenant_id filters matching authenticated user
• Data returned respects user roles and permissions
• No cross-tenant data leakage possible

Definition of Done:

• Data access code updated with RBAC and tenant filters
• Integration tests verify data isolation and access control
• Code reviewed and merged

Develop Governance Dashboard Components for Engineering Teams

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Create Angular components to display execution traces, regression test results, and remediation status for engineering users. Include search, filtering, and drill-down capabilities with RBAC and tenant scoping.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 4h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Dashboard displays execution traces and failure cluster insights
• Regression test results and remediation tracking are visible
• UI supports search, filtering, and drill-down into trace details

Definition of Done:

• Angular components implemented and integrated
• Unit and integration tests cover UI interactions and data binding
• UI passes accessibility and responsiveness tests

Enforce Tenant Data Partitioning in Database Schemas and Queries

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Modify database schemas and ORM queries to enforce tenant_id partitioning and filtering on all multi-tenant tables, including telemetry_events, detections, agent_traces, and failure_clusters. Implement PostgreSQL partitioning by tenant_id and event_timestamp.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• All relevant tables partitioned by tenant_id and time
• ORM queries include tenant_id filters by default
• No cross-tenant data access possible via API or DB

Definition of Done:

• Database migrations for partitioning created and tested
• Query layer enforces tenant scoping
• Integration tests verify tenant isolation

Implement Alert Delivery Status Logging and Dead-Letter Queue Handling

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 90e91dcc-6857-4449-b707-88e1f84be11b
• Critical path: No
• Description: Develop mechanisms to log alert delivery attempts, response codes, and statuses. Implement dead-letter queue for failed deliveries with retry and manual resend capabilities.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• All alert delivery attempts are logged with status and response details
• Failed deliveries are moved to dead-letter queue for retry
• Manual resend API or CLI tool is available for DLQ alerts

Definition of Done:

• Delivery logging and DLQ implemented and tested
• Integration tests verify retry and manual resend functionality
• Documentation updated for alert delivery monitoring

Implement CRUD APIs for Alert Integrations Management

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop FastAPI endpoints to create, read, update, and delete alert integrations. Support enabling/disabling endpoints, updating auth configs, and setting routing preferences per tenant and user role with RBAC enforcement.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• API supports CRUD operations on alert integrations with validation
• Supports enabling/disabling integrations and updating auth configs
• Enforces RBAC and tenant scoping on all operations

Definition of Done:

• CRUD endpoints implemented and integrated
• Unit and integration tests cover all API operations and RBAC
• API documentation updated

Implement PagerDuty Alert Delivery Connector

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop the integration connector module to send alerts to PagerDuty using their REST API with OAuth2 or API key authentication. Implement payload formatting, error handling, retry logic, and dead-letter queue for failed deliveries.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Alerts are sent to PagerDuty REST API with correct authentication
• Failed deliveries are retried with exponential backoff
• Failed alerts are moved to dead-letter queue after max retries

Definition of Done:

• Code implemented and reviewed
• Unit and integration tests cover connector functionality
• Documentation updated with usage and config details

Implement OpenAI API Client for Embedding Generation and Evaluation

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop LLMJudgeAdapter module to call OpenAI API endpoints for generating embeddings and evaluation scoring. Handle API authentication securely using Vault-stored keys.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• LLMJudgeAdapter calls OpenAI API for embeddings and evaluation with correct request/response handling
• API keys are securely retrieved from Vault and not stored in code
• Handles rate limiting with retries and exponential backoff

Definition of Done:

• OpenAI API client implemented and integrated
• Unit and integration tests cover success, retry, and error scenarios
• Documentation updated with usage and configuration

Implement PATCH /api/v1/tenant_configs Endpoint for Telemetry Configuration Updates

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): d798526c-b2a8-47b0-b674-9c620ed62968
• Critical path: No
• Description: Develop FastAPI endpoint to allow tenant admins to update telemetry configuration fields: event_types, sampling_rate, policy_rules, and semantic_thresholds. Enforce RBAC, validate input, and trigger cache invalidation.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• PATCH /api/v1/tenant_configs accepts partial updates with validation
• Updates persist to tenant_configs table with tenant_id scoping
• Configuration changes trigger cache invalidation and SDK refresh events

Definition of Done:

• Endpoint implemented with validation and persistence
• Unit and integration tests cover update scenarios and error handling
• API documentation updated

Implement Splunk Alert Delivery Connector via SIEM

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop the integration connector to deliver alerts to Splunk using SIEM connectors (Syslog/TCP). Implement authentication via HMAC or API keys, payload formatting, retry logic, and dead-letter queue handling.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (medium confidence)

Acceptance Criteria:

• Alerts are delivered to Splunk SIEM connectors with correct authentication
• Retries are implemented for transient failures
• Dead-letter queue captures permanently failed alerts

Definition of Done:

• Connector code implemented and tested
• Integration tests simulate alert delivery to Splunk
• Documentation includes configuration and usage

Implement Anthropic API Client for Embeddings and Evaluation

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop LLMJudgeAdapter module to call Anthropic API endpoints for embeddings, evaluation, and trace enrichment. Manage API authentication securely using Vault-stored keys.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• LLMJudgeAdapter calls Anthropic API with correct request/response handling
• API keys are securely retrieved from Vault
• Handles rate limits with retries and exponential backoff

Definition of Done:

• Anthropic API client implemented and integrated
• Unit and integration tests cover normal and error cases
• Documentation updated

Implement JSON Schema Validation and Caching for Policy Rules in SDK

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 9ae3e6ea-8be0-47a5-8ab3-fbe8caa49f00
• Critical path: No
• Description: Add JSON schema validation for policy violation detection rules and semantic thresholds in the SDK. Implement caching with TTL and refresh on config change events.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Policy rules are validated against JSON schemas before application
• Invalid rules are rejected and logged with errors
• Caching prevents redundant API calls and refreshes on config change

Definition of Done:

• Validation and caching implemented and tested
• Unit tests cover valid and invalid rule scenarios
• Documentation updated for SDK validation behavior

Implement RBAC and Tenant Scoping Middleware for Alert Integration APIs

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop middleware to enforce role-based access control and tenant scoping on alert integration APIs, ensuring only authorized users can manage integrations within their tenant context.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.8h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: security (medium confidence)

Acceptance Criteria:

• Middleware validates JWT tokens and extracts user roles
• API requests are scoped to tenant_id from token
• Unauthorized access attempts return 403 errors

Definition of Done:

• Middleware implemented and integrated
• Unit tests cover role and tenant enforcement
• Audit logging enabled for access events

Document On-Premise Deployment Differences and Manual Update Processes

• Type: documentation | Priority: high | Status: todo
• Critical path: No
• Description: Create detailed documentation describing feature availability differences between SaaS and on-premise deployments, including limitations on external API calls, local proxy usage, manual update procedures, and network isolation requirements.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.1h (1.8x factor)
• AI tools: Cursor
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation clearly explains feature differences and trade-offs
• Manual update steps are detailed and reproducible
• Security and network isolation guidelines are included

Definition of Done:

• Documentation written and reviewed
• Published in project docs repository
• Linked from onboarding and deployment guides

Implement Logging and Fallback for Anthropic API Integration

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 932afadc-dba0-4fc0-8690-9dad690c9b47
• Critical path: No
• Description: Add detailed logging of Anthropic API usage and errors. Implement fallback mechanisms to queue requests on persistent failures to ensure reliability.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• All API calls logged with request and response metadata
• Persistent failures enqueue requests for later retry
• Fallback integrates with existing task queue or Kafka

Definition of Done:

• Logging and fallback implemented and tested
• Integration tests verify fallback behavior
• Documentation updated

Implement Real-Time Propagation of Alert Integration Config Changes

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop mechanism to propagate changes to alert integration configurations promptly to the integration connectors, using Kafka or Redis pub/sub for config invalidation and update notification.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Config changes trigger update events to integration connectors
• Integration connectors reload updated configs without restart
• Propagation latency is under 5 seconds

Definition of Done:

• Propagation mechanism implemented and tested
• Integration connectors demonstrate dynamic config reload
• Monitoring alerts configured for propagation failures

Implement Audit Logging for Tenant Data Access

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop audit logging functionality to record all data access events per tenant, including user ID, resource type, resource ID, action, and timestamp. Store logs in audit_logs table and expose APIs for audit review.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• All data access actions are logged with tenant and user context
• Audit logs are stored securely and partitioned by tenant
• APIs allow authorized users to query audit logs

Definition of Done:

• Audit logging implemented and tested
• Integration tests verify log creation on data access
• Documentation updated with audit log usage

Implement Logging and Fallback Mechanisms for OpenAI API Integration

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): dcdad24d-d870-41bb-b4fd-c7c848c6d0c3
• Critical path: No
• Description: Add detailed logging of OpenAI API requests, responses, errors, and usage metrics. Implement fallback to queue for persistent failures to ensure reliability.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• All API calls are logged with request and response metadata
• Persistent failures enqueue requests for later retry
• Fallback queue integrates with existing task queue or Kafka

Definition of Done:

• Logging and fallback implemented and tested
• Integration tests verify fallback behavior
• Documentation updated for error handling

Implement SaaS Deployment Automation and Monitoring Setup

• Type: devops | Priority: high | Status: todo
• Critical path: No
• Description: Develop CI/CD pipelines using GitHub Actions for SaaS deployment with managed services. Configure monitoring stack with OpenTelemetry Collector, Prometheus, and Grafana dashboards for multi-tenant observability.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.3h (1.5x factor)
• AI tools: Cursor
• AI category: implementation (high confidence)

Acceptance Criteria:

• CI/CD pipelines deploy all services to managed cloud environment
• Monitoring stack collects metrics and traces with tenant context
• Dashboards provide role-based views for tenants

Definition of Done:

• CI/CD workflows implemented and tested
• Monitoring dashboards created and validated
• Documentation updated with deployment and monitoring instructions

Implement Kafka topic partitioning and scaling strategy for tenant and agent volume

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Design and configure Kafka topics with partitions keyed by tenant_id and agent_id to enable parallel consumption and isolation. Implement tooling to monitor partition usage and rebalance partitions dynamically. Document partitioning strategy for operations team.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.33h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Kafka topics partitioned by tenant_id and agent_id with configurable partition counts
• Partitions enable parallel consumption by multiple service instances
• Monitoring dashboards show partition distribution and lag
• Partition rebalancing does not cause data loss or downtime

Definition of Done:

• Kafka topics and partitions configured and tested in staging
• Monitoring dashboards created
• Documentation updated with partitioning strategy

Implement GET and PATCH /api/v1/tenant_configs endpoints for data retention policies

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop FastAPI endpoints GET /api/v1/tenant_configs to retrieve current tenant config including retention_policy_days, and PATCH /api/v1/tenant_configs to update retention durations and related compliance policies. Enforce RBAC for tenant admins, validate inputs, persist changes in tenant_configs table, and log changes in audit_logs.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.33h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• GET /api/v1/tenant_configs returns current config scoped to tenant
• PATCH /api/v1/tenant_configs updates retention_policy_days with validation
• RBAC restricts access to tenant admins
• Changes are logged in audit_logs with user and timestamp
• API responses conform to OpenAPI 3.1 spec

Definition of Done:

• Endpoints implemented with validation and auth
• Unit and integration tests cover success and error cases
• Audit logging verified
• API documentation updated

Create Dockerfiles for all backend microservices

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Write Dockerfiles for ingestion-gateway, detection, trace, evaluation, clustering, alerting, governance-api, and auth services using Python 3.14.6 base image and best practices.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 2.5h (2x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Dockerfiles build successfully without errors
• Containers run and expose expected ports
• Images are optimized for size and security

Definition of Done:

• Dockerfiles committed and tested locally
• Images pushed to local registry or Docker Hub
• Documentation updated with build and run instructions

Implement API Layer Tenant Scoping and RBAC Enforcement

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Enhance all API endpoints to enforce tenant scoping by extracting tenant_id from JWT tokens and applying RBAC policies to restrict data access. Implement middleware to reject requests with cross-tenant access attempts.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2.5h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: security (medium confidence)

Acceptance Criteria:

• API requests are scoped to tenant_id from JWT token
• RBAC middleware restricts access based on user roles
• Unauthorized cross-tenant requests return 403 errors

Definition of Done:

• Middleware implemented and integrated in all APIs
• Unit and integration tests cover tenant scoping and RBAC
• Audit logs capture access events

Optimize OpenSearch indices for multi-tenant trace and failure memory search

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Configure OpenSearch 3.0 index templates and mappings to support multi-tenant data isolation, efficient vector similarity search, and fast text queries. Tune shard counts, refresh intervals, and caching settings. Implement tenant_id filtering in queries. Validate search performance under load.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• OpenSearch indices support tenant-scoped queries with filters
• Vector search uses pgvector-compatible plugins or external integration
• Index settings optimized for query latency and throughput
• Search queries return results within SLA under load

Definition of Done:

• Index templates and settings applied and tested
• Search API queries validated for correctness and performance
• Documentation updated for index management

Implement Local Proxies for OpenAI and Anthropic API Calls in On-Prem Deployment

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop local proxy services to securely handle OpenAI and Anthropic API calls within on-premise and air-gapped deployments. Ensure proxies enforce strict network controls and support manual update mechanisms.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Local proxies forward LLM API requests securely within network
• Proxies enforce authentication and rate limiting
• Manual update process for proxy configurations is documented

Definition of Done:

• Proxy services implemented and tested in on-prem environment
• Security controls verified by pen-testing
• Documentation covers deployment and update procedures

Write unit and integration tests for detection rule APIs and dynamic propagation

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Develop pytest unit tests for detection rule CRUD endpoints covering validation, RBAC, tenant scoping, and error handling. Create integration tests simulating rule creation, update, and propagation to DetectionProcessor with Kafka/Redis. Verify API responses and database state consistency.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.625h (1.6x factor)
• AI tools: GitHub Copilot
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover all API input validation and auth scenarios
• Integration tests verify persistence and propagation of rule changes
• Tests pass with coverage >80% for detection_rules module

Definition of Done:

• Test code implemented and integrated in CI
• All tests pass reliably
• Test coverage meets project standards

Implement Kafka Consumer Health Monitoring and Partition Management

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop monitoring and management tools to track Kafka consumer health, partition lag, and fault tolerance. Integrate with Prometheus metrics and alerting for ingestion gateway and downstream consumers.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Kafka consumer lag and health metrics are collected and exposed
• Alerts trigger on partition failures or lag breaches
• Monitoring integrates with Prometheus and Grafana dashboards

Definition of Done:

• Monitoring code implemented and integrated
• Tested with simulated partition failures and lag
• Documentation updated with monitoring setup

Refactor ingestion and processing services to be stateless and support horizontal scaling

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Modify ingestion gateway, detection processor, trace capture, and evaluation services to remove local state, use external caches (Redis) and message broker (Kafka) for state management. Ensure services can run multiple instances behind load balancers with consistent tenant partitioning. Implement health checks and graceful shutdown.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Claude Code (Anthropic)
• AI category: refactoring (medium confidence)

Acceptance Criteria:

• Services run multiple instances concurrently without state conflicts
• Kafka partitions and Redis caches are used for shared state
• Health endpoints report status for orchestration
• Services support graceful shutdown and restart

Definition of Done:

• Code refactored and deployed in multi-instance environment
• Integration tests verify no state conflicts under concurrency
• Documentation updated for deployment and scaling

Write unit and integration tests for RBAC middleware and enforcement

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Create pytest unit tests for RBAC middleware covering role validation, permission checks, and tenant scoping. Develop integration tests simulating authorized and unauthorized API calls, verifying 403 responses and audit log entries. Test data query filtering for tenant isolation.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.25h (1.6x factor)
• AI tools: GitHub Copilot
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover all RBAC middleware logic paths
• Integration tests verify API and data layer enforcement
• Unauthorized access attempts return 403 and are logged
• Test coverage meets project standards

Definition of Done:

• Tests implemented and integrated in CI
• All tests pass reliably
• Coverage reports generated

Document API usage and setup instructions for developers

• Type: documentation | Priority: medium | Status: todo
• Depends on (complete these first): e7f64d84-eb40-4fb1-a598-63d65f943d4b
• Critical path: No
• Description: Write comprehensive documentation covering API access, authentication, versioning, and usage examples, ensuring developers can access docs locally and remotely.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 1.5h (2x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers all API endpoints and auth methods
• Examples provided for common API calls
• Docs accessible both locally and via hosted site

Definition of Done:

• Documentation committed and reviewed
• Docs linked in project README
• Reviewed by developer team

Optimize PostgreSQL schemas with partitioning and indexes for large telemetry and trace data

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Review and implement range partitioning on telemetry_events, agent_traces, and failure_clusters tables by tenant_id and event_timestamp. Add or optimize indexes on tenant_id, event_type, agent_id, and vector columns using pgvector. Write Alembic migrations for schema changes and test performance improvements.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Claude Code (Anthropic)
• AI category: implementation (medium confidence)

Acceptance Criteria:

• Tables partitioned by tenant_id and event_timestamp with minimal query impact
• Indexes on tenant_id, event_type, agent_id, and vector columns improve query latency
• Alembic migrations apply cleanly with rollback support
• Performance tests show query latency within SLA

Definition of Done:

• Schema changes implemented and tested
• Migration scripts versioned and documented
• Performance benchmarks documented

Write unit and integration tests for JWT and OAuth2 authentication flows

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Develop pytest unit tests for JWT token validation, OAuth2 flows, token refresh, and revocation. Create integration tests simulating authenticated and unauthenticated API requests, verifying RBAC claims and tenant scoping. Test failure logging and monitoring hooks.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.25h (1.6x factor)
• AI tools: GitHub Copilot
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover all auth middleware code paths
• Integration tests verify token validation and RBAC enforcement
• Authentication failure logging is tested
• Test coverage meets project standards

Definition of Done:

• Tests implemented and integrated in CI
• All tests pass reliably
• Coverage reports generated

Implement POST /api/v1/telemetry/events endpoint in Ingestion Gateway

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Create FastAPI endpoint to accept telemetry events with fields: agent_id (UUID), event_type (enum), event_timestamp (ISO8601), payload (JSON). Validate payload, authenticate tenant via JWT, and return acceptance status.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Endpoint accepts valid telemetry events and returns 202 Accepted
• Rejects malformed payloads with 422 Unprocessable Entity
• Tenant authentication enforced via JWT Bearer tokens

Definition of Done:

• Endpoint implemented and integrated
• Unit and integration tests cover validation and auth
• OpenAPI spec updated with endpoint details

Create POST /api/v1/telemetry/events/replay endpoint for admin replay requests

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement FastAPI endpoint allowing admin users to submit a list of telemetry event IDs for replay, returning a replay job ID for tracking.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (1.8x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Endpoint accepts list of event IDs and returns replay_job_id
• Only admin users authorized via JWT can access endpoint
• Input validation rejects invalid or unauthorized event IDs

Definition of Done:

• Endpoint implemented and tested
• Unit and integration tests cover auth and validation
• OpenAPI spec updated

Implement Scheduled Batch Detection Jobs with Celery

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop Celery scheduled tasks to run batch detection checks on telemetry data at configurable intervals (minutes to hours) for trend analysis. Ensure batch jobs do not impact real-time detection throughput.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Batch detection jobs run on configured schedules
• Batch results are stored and accessible via API
• Batch jobs do not degrade real-time detection performance

Definition of Done:

• Batch detection tasks implemented and tested
• Integration tests verify scheduling and data correctness
• Monitoring alerts on batch job failures configured

Create GitHub Actions workflows for CI build and test

• Type: devops | Priority: high | Status: todo
• Critical path: No
• Description: Configure GitHub Actions workflows to build Docker images, run backend unit and integration tests, and report status on pull requests.
• Estimate: 8 hours
• Story Points: 3
• Traditional estimate: 8h | AI-accelerated: 6.4h (1.25x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Pull requests trigger build and test workflows
• Failed tests cause workflow failure and block merges
• Build status badges visible in PRs and main branch

Definition of Done:

• Workflows committed and tested
• Build and test logs accessible
• Team notified of CI status

Implement GET /api/v1/tenant_configs Endpoint for Telemetry Configuration Retrieval

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop FastAPI endpoint to retrieve tenant-specific telemetry configuration including event types, sampling rate, policy rules, and semantic thresholds. Enforce RBAC and tenant scoping.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• GET /api/v1/tenant_configs returns current telemetry config for authenticated tenant
• Response includes event_types (list), sampling_rate (float), policy_rules (dict), semantic_thresholds (dict)
• Unauthorized access is denied with 403 error

Definition of Done:

• Endpoint implemented and integrated
• Unit and integration tests cover success and error cases
• API documentation updated with endpoint details

Implement Monitoring and Logging for Batch Detection Jobs

• Type: devops | Priority: high | Status: todo
• Critical path: No
• Description: Set up monitoring for batch detection job execution, including success/failure rates, execution duration, and retry counts. Integrate with Prometheus metrics and alerting to notify on failures or SLA breaches.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.1h (1.8x factor)
• AI tools: Cursor
• AI category: implementation (high confidence)

Acceptance Criteria:

• Batch job metrics are exposed to Prometheus
• Alerts trigger on job failures or execution delays
• Logs provide detailed batch job execution traces

Definition of Done:

• Monitoring and alerting configured and tested
• Documentation updated with monitoring setup
• Alerts verified with simulated failures

Implement RBAC enforcement middleware for API and data layers

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop FastAPI middleware that enforces RBAC policies on all API endpoints and database queries. Load RBAC policies from secure storage, validate user roles and permissions per tenant, filter data queries accordingly, and return 403 errors on unauthorized access. Integrate audit logging for access attempts.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 3.33h (1.2x factor)
• AI tools: Claude Code (Anthropic)
• AI category: security (medium confidence)

Acceptance Criteria:

• API requests are authorized based on user roles and tenant scope
• Data queries are filtered to prevent cross-tenant or unauthorized access
• Unauthorized requests return 403 Forbidden errors
• RBAC policies are configurable and stored securely
• Access attempts are logged in audit logs

Definition of Done:

• Middleware implemented and integrated in API services
• Unit and integration tests cover authorization logic
• Code reviewed and merged
• API documentation updated

Implement RBAC Middleware for Governance API Endpoints

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Develop FastAPI middleware to enforce role-based access control on all governance API endpoints. Middleware must validate JWT tokens, extract user roles, enforce tenant scoping, and restrict data access accordingly.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2.5h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: security (high confidence)

Acceptance Criteria:

• RBAC middleware intercepts all governance API requests and enforces role-based restrictions
• Unauthorized requests return 403 Forbidden with structured error payload
• Tenant scoping is strictly enforced, users cannot access data outside their tenant

Definition of Done:

• Middleware implemented and integrated with governance API
• Unit and integration tests cover role enforcement and tenant scoping
• Code review completed and merged

Implement Tenant Retention Policy Configuration API

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop FastAPI endpoints under /api/v1/tenant_configs to allow tenant admins to set data retention durations, deletion policies, and compliance settings. Validate inputs and enforce RBAC and tenant scoping.
• Estimate: 3 hours
• Story Points: 3
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• API supports GET and PATCH methods for tenant retention config
• Input validation enforces allowed retention ranges and policy formats
• RBAC restricts access to tenant admins only

Definition of Done:

• API endpoints implemented and tested
• Integration tests verify RBAC and data validation
• Documentation updated with API usage

Implement Async Microservice for Trace Capture and Indexing

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop the trace capture microservice consuming trace events from Kafka asynchronously. Store traces and steps in PostgreSQL with tenant isolation, compute embeddings using LLM APIs, and index metadata in OpenSearch. Emit OpenTelemetry traces and Prometheus metrics.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.7h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Trace service consumes Kafka events asynchronously
• Traces and steps stored with tenant_id partitioning
• Embeddings computed and indexed in PostgreSQL and OpenSearch

Definition of Done:

• Trace microservice implemented and tested
• Integration tests verify storage and indexing
• Monitoring and health checks implemented

Write unit and integration tests for data deletion and export compliance features

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Create pytest unit tests for data retention tasks covering deletion logic, tenant isolation, and error handling. Develop integration tests for export API validating format correctness, tenant scoping, and audit logging. Verify retry and DLQ behavior on task failures.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.25h (1.6x factor)
• AI tools: GitHub Copilot
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover all deletion and export task code paths
• Integration tests validate API responses and audit log entries
• Tests verify retry and DLQ mechanisms function correctly
• Test coverage meets project standards

Definition of Done:

• Tests implemented and integrated in CI
• All tests pass reliably
• Coverage reports generated

Write unit and integration tests for detection rule APIs and propagation

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): ec88b461-6f5c-44e4-873d-36f682f674b6, 9d29eb82-98ae-48f8-ac35-c5058537e715
• Critical path: No
• Description: Develop comprehensive tests covering detection rule CRUD APIs, RBAC enforcement, validation, and real-time propagation to detection processor.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (1.6x factor)
• AI tools: GitHub Copilot
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Tests cover all API endpoints with valid and invalid inputs
• RBAC enforcement verified for admin-only access
• Propagation mechanism tested for timely config updates

Definition of Done:

• Test coverage meets project standards
• Tests pass in CI pipeline
• Test documentation updated

Implement Secure Data Fetching with RBAC and Tenant Scoping for Engineering Dashboard

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Ensure all data fetched by engineering dashboard components is securely retrieved with RBAC enforcement and tenant scoping applied at API and query layers.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.7h (1.2x factor)
• AI tools: GitHub Copilot
• AI category: security (high confidence)