Feature Strategy

Overview

• Total features: 14

Core Features

Kubernetes Workload Promotion Preview

• ID: feature-1
• Priority: high
• Category: core
• Description: Allows platform teams to preview exactly what will change in Kubernetes workloads before promoting them to production, including the cost impact.
• User impact: Enables users to make informed decisions about workload promotions, reducing risk and unexpected costs.

Requirements:

• Preview diffs of workload changes between staging and production
• Show cost impact of workload promotions
• Support Git-first workflow for promotion actions
• Display promotion risk assessment

Technical requirements:

• Integration with Kubernetes APIs for read-only cluster state
• Cost estimation engine integration
• GitOps orchestration to reflect promotion actions
• UI to visualize diffs and cost previews

Lightweight Read-Only Kubernetes Agent

• ID: feature-2
• Priority: high
• Category: core
• Description: A lightweight, read-only Kubernetes agent installed per cluster that collects data without mutating the cluster, ensuring security and trust.
• User impact: Provides secure, trusted data collection from clusters without risk of unintended changes.

Requirements:

• Agent must be installed via Helm
• Agent operates in read-only mode
• No direct mutation of cluster resources
• Secure communication with SaaS control plane

Technical requirements:

• Helm chart packaging for agent deployment
• Kubernetes RBAC scoped for read-only access
• Secure TLS communication channels
• Low resource footprint

GitOps Orchestration for Promotion Actions

• ID: feature-3
• Priority: high
• Category: core
• Description: All promotion actions flow through Git, making every change auditable and traceable by design.
• User impact: Ensures transparency and auditability of promotion actions, increasing trust and compliance.

Requirements:

• Integration with Git repositories for promotion workflows
• Automatic commit and pull request creation for promotions
• Audit trail of all promotion changes via Git history

Technical requirements:

• Git API integration
• Webhook or polling mechanisms for Git events
• Conflict detection and resolution support

LLM Explanation Engine for Promotion Risk and Cost

• ID: feature-4
• Priority: high
• Category: core
• Description: An explanation engine powered by a large language model that surfaces plain-English reasoning for why a promotion is risky and what it will cost.
• User impact: Helps users understand complex promotion implications, improving decision-making.

Requirements:

• Generate human-readable explanations of risk factors
• Explain cost impact in accessible language
• Integrate with promotion preview UI

Technical requirements:

• Integration with an LLM API or self-hosted model
• Natural language generation capabilities
• Contextual data extraction from promotion metadata

Cluster Management UI

• ID: feature-5
• Priority: medium
• Category: core
• Description: User interface for managing multiple Kubernetes clusters, including agent installation status and cluster metadata.
• User impact: Simplifies management of multiple clusters and visibility into agent deployment.

Requirements:

• List and detail view of clusters
• Agent installation status monitoring
• Cluster metadata display

Technical requirements:

• UI components for cluster management
• Backend APIs to fetch cluster and agent data

Helm Management UI

• ID: feature-6
• Priority: medium
• Category: core
• Description: UI to manage Helm charts and releases within clusters, facilitating easier workload promotions and rollbacks.
• User impact: Improves user ability to manage Helm-based workloads and promotions.

Requirements:

• View installed Helm releases
• Manage Helm chart versions
• Support promotion workflows involving Helm

Technical requirements:

• Integration with Helm APIs or CLI
• UI components for Helm release management

Deep FinOps Billing Reconciliation

• ID: feature-7
• Priority: high
• Category: core
• Description: Capability to reconcile cloud billing data deeply with Kubernetes workload cost estimations to identify cost spikes and anomalies.
• User impact: Enables users to understand and control cloud costs related to Kubernetes workloads.

Requirements:

• Import and process cloud billing data from AWS, GCP, Azure
• Map billing data to Kubernetes workloads
• Detect and alert on cost anomalies post-deployment

Technical requirements:

• Data ingestion pipelines for billing data
• Cost attribution algorithms
• Dashboard and alerting systems

Integrations:

• Cloud provider billing APIs

Auto-Remediation with Human Approval

• ID: feature-8
• Priority: medium
• Category: core
• Description: Automated remediation actions triggered by detected issues, requiring human approval before execution to maintain control.
• User impact: Reduces operational risk by automating fixes while preserving human oversight.

Requirements:

• Define remediation rules and triggers
• Require explicit human approval before remediation
• Audit trail of remediation actions

Technical requirements:

• Workflow engine for remediation processes
• UI for approval workflows
• Integration with GitOps for remediation changes

Integration Features

Cloud Provider Billing APIs Integration

• ID: integration-1
• Priority: high
• Category: integration
• Description: Integrate with cloud provider billing APIs to import cost data for FinOps reconciliation and cost impact previews.
• User impact: Provides accurate cost data to users for budgeting and cost control.

Requirements:

• Support AWS, GCP, and Azure billing APIs
• Secure API authentication and authorization
• Regular data synchronization

Technical requirements:

• API client implementations for each cloud provider
• Data normalization and mapping to Kubernetes workloads

Integrations:

• Cloud provider billing APIs

Infrastructure Features

Hybrid Delivery Model Infrastructure

• ID: infra-1
• Priority: high
• Category: infrastructure
• Description: Infrastructure supporting a hybrid delivery model with a SaaS control plane and per-cluster agents deployed via Helm.
• User impact: Ensures reliable and secure operation across distributed Kubernetes environments.

Requirements:

• SaaS control plane hosting with high availability
• Agent deployment and upgrade via Helm
• Secure communication between control plane and agents

Technical requirements:

• Cloud hosting for control plane
• Helm packaging and versioning
• TLS and authentication mechanisms

Non-Functional Requirements

Security and SOC2 Compliance

• ID: nfr-1
• Priority: high
• Category: nfr
• Description: Non-negotiable security requirements including SOC2 compliance, strict read-only boundaries, scoped RBAC, and zero direct cluster mutation.
• User impact: Builds enterprise trust by ensuring data security and compliance.

Requirements:

• Implement scoped RBAC with least privilege
• Ensure agent is strictly read-only
• Maintain full audit trails of all actions
• Compliance with SOC2 security standards

Technical requirements:

• Role-based access control enforcement
• Immutable audit logs
• Security monitoring and alerting

Performance and Scalability

• ID: nfr-2
• Priority: high
• Category: nfr
• Description: Ensure the platform performs efficiently and scales to support multiple clusters and large workloads.
• User impact: Provides smooth user experience and supports growth without degradation.

Requirements:

• Low latency UI and API responses
• Scalable backend to handle multiple clusters
• Efficient data processing pipelines

Technical requirements:

• Horizontal scaling of control plane services
• Caching and optimization strategies
• Load balancing

Reliability and Availability

• ID: nfr-3
• Priority: high
• Category: nfr
• Description: Ensure high availability and reliability of the SaaS control plane and agent communication.
• User impact: Minimizes downtime and ensures continuous operation for users.

Requirements:

• Redundant control plane components
• Graceful agent reconnection and failover
• Monitoring and alerting on system health

Technical requirements:

• Distributed system design
• Health checks and automated recovery
• Logging and observability

Time-to-First-Value within 7 Days

• ID: nfr-4
• Priority: high
• Category: nfr
• Description: MVP goal to enable users to achieve meaningful value from the product within 7 days of signup.
• User impact: Accelerates user adoption and satisfaction by delivering early value.

Requirements:

• Simple onboarding process
• Quick agent installation via Helm
• Fast initial data sync and promotion previews

Technical requirements:

• Streamlined installation and configuration workflows
• Efficient data ingestion and processing
• User guidance and documentation

KubeCost Preview — Enterprise-Grade Technical Architecture

1. SYSTEM OVERVIEW

High-Level System Design

KubeCost Preview is a hybrid-delivered, Git-first, security-first Kubernetes control plane for Platform Engineers, SREs, and Engineering Managers. The system enables previewing workload promotions, cost impact, and risks before production deployment. Its core trust model is a read-only Kubernetes agent (per cluster), SaaS control plane, GitOps orchestration, a cost and billing engine, and an LLM-powered explanation engine, all forming a secure, auditable, and SOC2-compliant architecture.

Major Components:

• SaaS Control Plane: UI, APIs, business logic, cost engine, LLM explanation engine, GitOps orchestration, audit logging, and billing reconciliation.
• Kubernetes Read-Only Agent: Helm-installed, cluster-scoped, read-only agent, sending cluster state and resource metadata securely to the control plane.
• GitOps Integration: All promotions and remediations are performed via Git commits/PRs, ensuring auditable, intent-driven change.
• Cost & FinOps Engine: Aggregates cluster state with cloud billing data to preview and reconcile costs.
• LLM Explanation Engine: Provides plain-English explanations for risk and cost assessments.
• UI: Cluster management, Helm management, workload diff/cost preview, approval workflows, and audit logs.

System Boundaries:

• No direct cluster mutation: All changes flow through Git.
• Per-cluster agent is read-only, enforced by RBAC and agent code.
• SOC2 compliance as a non-negotiable constraint.

Data Flows:

1. Agent periodically syncs (read-only) cluster state to control plane.
2. User initiates workload promotion in UI → control plane computes diff/cost preview → LLM generates explanations.
3. On approval, GitOps Orchestrator commits changes to Git.
4. Agent observes Git, surfaces status and promotion readiness.
5. Billing engine ingests cloud billing data, reconciles with cluster state for FinOps and anomaly detection.
6. Auto-remediation triggers create Git PRs, requiring human approval in UI.

Integration Points:

• Git providers (GitHub, GitLab, etc.)
• Cloud billing APIs (AWS, GCP, Azure)
• LLM provider (OpenAI/Azure OpenAI, or self-hosted)
• Kubernetes APIs (v1.36.1+)
• Helm APIs/SDK

High-Level Architecture Diagram

flowchart TD
 A["Operator UI (React 19)"]
 B["API Gateway (Go 1.22, gRPC+REST)"]
 C["Auth Service (OIDC)"]
 D["Promotion Preview Service"]
 E["Cost Engine"]
 F["LLM Explanation Engine"]
 G["GitOps Orchestrator"]
 H["Audit Log Service"]
 I["Cluster Management Service"]
 J["Helm Management Service"]
 K["Billing Reconciliation Service"]
 L["Auto-Remediation Service"]
 M["DB (PostgreSQL 16)"]
 N["Cache (Redis 7.2)"]
 O["Per-Cluster Agent (Go, Read-only, Helm 3.15)"]
 P["Git Provider"]
 Q["Cloud Billing API"]
 R["Kubernetes API (Cluster)"]

 A-->|HTTPS|B
 B-->|JWT/OIDC|C
 B-->|Promotion preview|D
 D-->|Cost estimate|E
 D-->|LLM explanation|F
 D-->|GitOps PR|G
 B-->|Audit logs|H
 B-->|Cluster mgmt|I
 B-->|Helm mgmt|J
 B-->|FinOps|K
 K-->|Billing API|Q
 G-->|Git API|P
 O-->|Agent sync|I
 O-->|Helm data|J
 O-->|Cluster state|D
 O-->|Secure comms|B
 I-->|Cluster metadata|M
 J-->|Helm metadata|M
 H-->|Audit logs|M
 B-->|Cache|N
 D-->|DB|M
 G-->|DB|M
 K-->|DB|M
 L-->|Approval workflow|B
 L-->|Git PR|P
 O-->|Kubernetes API|R

2. TECHNOLOGY STACK

Selection Rationale:

• Security, auditability, and SOC2 compliance are top priorities.
• Deep Kubernetes integration (v1.36.1+), GitOps, and cloud billing reconciliation require robust, performant, and cloud-native technologies.
• LLM integration and cost engine require scalable, async, and type-safe backend.
• UI must be modern, secure, and operator-focused.

Primary Recommendations:

Compatibility Considerations:

• All components are fully compatible with Kubernetes 1.36.1 and latest Helm.
• PostgreSQL 16 supports all audit/compliance requirements.
• Go 1.22 is stable and widely supported for cloud-native backends and agents.

3. ARCHITECTURE PATTERNS

Primary Pattern:

• Microservices — Modular services for UI/API, Promotion Preview, Cost Engine, LLM Explanation, GitOps Orchestration, Audit, Billing, and Remediation, deployed as independent containers, communicating via gRPC/REST.

Supporting Patterns:

• Hybrid Delivery: SaaS control plane + per-cluster agent.
• Event-Driven: Audit trails, billing ingestion, and remediation workflows operate via event bus (Redis Pub/Sub or NATS, if extended).
• CQRS: Command (promotion/remediation) and Query (preview, audit, cost) separation for scalability and auditability.
• API Gateway: Central entry point for UI/API, authentication, and request routing.
• GitOps: All cluster mutations go through Git commits/PRs, not direct API calls, ensuring auditability and intent-driven change.
• Immutable Audit Log: Write-once audit logs in PostgreSQL, append-only, for SOC2.

Pattern Rationale:

• Microservices allow scaling cost engine, LLM, and GitOps orchestrator independently.
• Event-driven workflows enable async processing for billing and remediation.
• Hybrid delivery enables secure, enterprise adoption (SaaS + on-prem agent).
• CQRS supports high auditability and performance for operator workflows.

4. DATABASE DESIGN

Feature-to-Table Mapping

Complete Schema Design

clusters

• id (UUID, PK, DEFAULT gen_random_uuid())
• name (VARCHAR(200), NOT NULL)
• cloud_provider (ENUM: 'aws', 'gcp', 'azure', NOT NULL)
• region (VARCHAR(100))
• created_at (TIMESTAMP, DEFAULT NOW())
• updated_at (TIMESTAMP)
• status (ENUM: 'active', 'inactive', 'pending', 'error', DEFAULT 'pending')
• agent_id (UUID, FK to cluster_agents.id)
• metadata (JSONB)
• INDEX idx_clusters_cloud_provider ON clusters(cloud_provider)
• UNIQUE (name, cloud_provider)

cluster_agents

• id (UUID, PK, DEFAULT gen_random_uuid())
• cluster_id (UUID, FK to clusters.id, UNIQUE)
• version (VARCHAR(50), NOT NULL)
• installed_at (TIMESTAMP, DEFAULT NOW())
• last_heartbeat (TIMESTAMP)
• status (ENUM: 'online', 'offline', 'error', DEFAULT 'offline')
• helm_release_name (VARCHAR(100))
• INDEX idx_agents_status ON cluster_agents(status)

cluster_snapshots

• id (UUID, PK, DEFAULT gen_random_uuid())
• cluster_id (UUID, FK to clusters.id)
• snapshot_time (TIMESTAMP, DEFAULT NOW())
• resources (JSONB, NOT NULL) -- serialized k8s resource states
• agent_version (VARCHAR(50))
• INDEX idx_snapshots_cluster_time ON cluster_snapshots(cluster_id, snapshot_time DESC)

workload_promotions

• id (UUID, PK, DEFAULT gen_random_uuid())
• cluster_id (UUID, FK to clusters.id)
• user_id (UUID, FK to users.id)
• workload_name (VARCHAR(200), NOT NULL)
• source_namespace (VARCHAR(100), NOT NULL)
• target_namespace (VARCHAR(100), NOT NULL)
• git_commit_id (UUID, FK to git_commits.id)
• status (ENUM: 'pending', 'previewed', 'approved', 'rejected', 'completed', 'failed', DEFAULT 'pending')
• requested_at (TIMESTAMP, DEFAULT NOW())
• approved_at (TIMESTAMP)
• completed_at (TIMESTAMP)
• cost_preview_id (UUID, FK to promotion_costs.id)
• risk_assessment_id (UUID, FK to promotion_risks.id)
• llm_explanation_id (UUID, FK to llm_explanations.id)
• INDEX idx_promotions_cluster_status ON workload_promotions(cluster_id, status)

workload_diffs

• id (UUID, PK, DEFAULT gen_random_uuid())
• promotion_id (UUID, FK to workload_promotions.id)
• diff_json (JSONB, NOT NULL)
• created_at (TIMESTAMP, DEFAULT NOW())
• INDEX idx_diffs_promotion ON workload_diffs(promotion_id)

promotion_costs

• id (UUID, PK, DEFAULT gen_random_uuid())
• promotion_id (UUID, FK to workload_promotions.id, UNIQUE)
• estimated_cost_delta (NUMERIC(16,4), NOT NULL)
• baseline_cost (NUMERIC(16,4), NOT NULL)
• projected_cost (NUMERIC(16,4), NOT NULL)
• currency (VARCHAR(10), DEFAULT 'USD')
• generated_at (TIMESTAMP, DEFAULT NOW())
• details (JSONB)
• INDEX idx_costs_promotion ON promotion_costs(promotion_id)

promotion_risks

• id (UUID, PK, DEFAULT gen_random_uuid())
• promotion_id (UUID, FK to workload_promotions.id, UNIQUE)
• risk_level (ENUM: 'low', 'medium', 'high', 'critical', NOT NULL)
• risk_factors (JSONB, NOT NULL)
• assessed_at (TIMESTAMP, DEFAULT NOW())
• INDEX idx_risks_promotion ON promotion_risks(promotion_id)

llm_explanations

• id (UUID, PK, DEFAULT gen_random_uuid())
• promotion_id (UUID, FK to workload_promotions.id, UNIQUE)
• explanation_text (TEXT, NOT NULL)
• generated_at (TIMESTAMP, DEFAULT NOW())

git_repos

• id (UUID, PK, DEFAULT gen_random_uuid())
• provider (ENUM: 'github', 'gitlab', 'bitbucket', 'azure_devops', NOT NULL)
• repo_url (VARCHAR(512), NOT NULL)
• access_token_encrypted (BYTEA, NOT NULL)
• created_at (TIMESTAMP, DEFAULT NOW())
• INDEX idx_gitrepos_provider ON git_repos(provider)

git_commits

• id (UUID, PK, DEFAULT gen_random_uuid())
• repo_id (UUID, FK to git_repos.id)
• commit_hash (VARCHAR(80), NOT NULL)
• author (VARCHAR(200))
• message (TEXT)
• committed_at (TIMESTAMP, DEFAULT NOW())
• promotion_id (UUID, FK to workload_promotions.id)
• INDEX idx_commits_repo_hash ON git_commits(repo_id, commit_hash)

git_pull_requests

• id (UUID, PK, DEFAULT gen_random_uuid())
• repo_id (UUID, FK to git_repos.id)
• pr_number (INT, NOT NULL)
• title (VARCHAR(255))
• description (TEXT)
• created_at (TIMESTAMP, DEFAULT NOW())
• merged_at (TIMESTAMP)
• status (ENUM: 'open', 'merged', 'closed', DEFAULT 'open')
• promotion_id (UUID, FK to workload_promotions.id)
• INDEX idx_prs_repo_status ON git_pull_requests(repo_id, status)

helm_charts

• id (UUID, PK, DEFAULT gen_random_uuid())
• name (VARCHAR(200), NOT NULL)
• version (VARCHAR(50), NOT NULL)
• repo_url (VARCHAR(512))
• metadata (JSONB)
• created_at (TIMESTAMP, DEFAULT NOW())
• UNIQUE (name, version)

helm_releases

• id (UUID, PK, DEFAULT gen_random_uuid())
• cluster_id (UUID, FK to clusters.id)
• chart_id (UUID, FK to helm_charts.id)
• namespace (VARCHAR(100), NOT NULL)
• release_name (VARCHAR(200), NOT NULL)
• version (VARCHAR(50), NOT NULL)
• status (ENUM: 'deployed', 'failed', 'pending', 'superseded', 'deleted', 'unknown', DEFAULT 'deployed')
• values (JSONB)
• deployed_at (TIMESTAMP, DEFAULT NOW())
• INDEX idx_helmreleases_cluster_ns ON helm_releases(cluster_id, namespace)

billing_accounts

• id (UUID, PK, DEFAULT gen_random_uuid())
• cloud_provider (ENUM: 'aws', 'gcp', 'azure', NOT NULL)
• account_id (VARCHAR(100), NOT NULL)
• display_name (VARCHAR(200))
• credentials_encrypted (BYTEA, NOT NULL)
• created_at (TIMESTAMP, DEFAULT NOW())
• INDEX idx_billingaccounts_provider ON billing_accounts(cloud_provider)

billing_imports

• id (UUID, PK, DEFAULT gen_random_uuid())
• billing_account_id (UUID, FK to billing_accounts.id)
• imported_at (TIMESTAMP, DEFAULT NOW())
• status (ENUM: 'pending', 'processing', 'completed', 'error', DEFAULT 'pending')
• raw_data (BYTEA)
• error_message (TEXT)

billing_records

• id (UUID, PK, DEFAULT gen_random_uuid())
• billing_account_id (UUID, FK to billing_accounts.id)
• record_time (TIMESTAMP, NOT NULL)
• resource_id (VARCHAR(255))
• cost (NUMERIC(16,4), NOT NULL)
• currency (VARCHAR(10), DEFAULT 'USD')
• category (VARCHAR(100))
• details (JSONB)
• INDEX idx_billingrecords_account_time ON billing_records(billing_account_id, record_time DESC)

cost_attributions

• id (UUID, PK, DEFAULT gen_random_uuid())
• cluster_id (UUID, FK to clusters.id)
• billing_record_id (UUID, FK to billing_records.id)
• k8s_resource_id (VARCHAR(255))
• attributed_cost (NUMERIC(16,4), NOT NULL)
• attribution_time (TIMESTAMP, DEFAULT NOW())
• INDEX idx_costattr_cluster_resource ON cost_attributions(cluster_id, k8s_resource_id)

remediation_rules

• id (UUID, PK, DEFAULT gen_random_uuid())
• name (VARCHAR(200), NOT NULL)
• cluster_id (UUID, FK to clusters.id)
• trigger_condition (JSONB, NOT NULL)
• action_template (JSONB, NOT NULL)
• enabled (BOOLEAN, DEFAULT TRUE)
• created_at (TIMESTAMP, DEFAULT NOW())

remediation_actions

• id (UUID, PK, DEFAULT gen_random_uuid())
• rule_id (UUID, FK to remediation_rules.id)
• detected_at (TIMESTAMP, DEFAULT NOW())
• status (ENUM: 'pending', 'approved', 'rejected', 'executed', 'failed', DEFAULT 'pending')
• details (JSONB)
• approval_id (UUID, FK to remediation_approvals.id)
• audit_log_id (UUID, FK to audit_logs.id)
• INDEX idx_remediationactions_rule_status ON remediation_actions(rule_id, status)

remediation_approvals

• id (UUID, PK, DEFAULT gen_random_uuid())
• action_id (UUID, FK to remediation_actions.id)
• approver_id (UUID, FK to users.id)
• approved_at (TIMESTAMP)
• status (ENUM: 'pending', 'approved', 'rejected', DEFAULT 'pending')
• comment (TEXT)

users

• id (UUID, PK, DEFAULT gen_random_uuid())
• email (VARCHAR(200), UNIQUE, NOT NULL)
• display_name (VARCHAR(200))
• created_at (TIMESTAMP, DEFAULT NOW())
• last_login (TIMESTAMP)
• oidc_subject (VARCHAR(255), UNIQUE)
• status (ENUM: 'active', 'disabled', 'pending', DEFAULT 'active')

rbac_roles

• id (UUID, PK, DEFAULT gen_random_uuid())
• name (VARCHAR(100), NOT NULL)
• permissions (JSONB, NOT NULL)
• created_at (TIMESTAMP, DEFAULT NOW())
• UNIQUE (name)

rbac_role_bindings

• id (UUID, PK, DEFAULT gen_random_uuid())
• role_id (UUID, FK to rbac_roles.id)
• user_id (UUID, FK to users.id)
• cluster_id (UUID, FK to clusters.id)
• bound_at (TIMESTAMP, DEFAULT NOW())
• UNIQUE (role_id, user_id, cluster_id)

audit_logs

• id (UUID, PK, DEFAULT gen_random_uuid())
• user_id (UUID, FK to users.id)
• action (VARCHAR(200), NOT NULL)
• resource_type (VARCHAR(100), NOT NULL)
• resource_id (UUID)
• details (JSONB)
• created_at (TIMESTAMP, DEFAULT NOW())
• INDEX idx_auditlogs_user_action ON audit_logs(user_id, action)

Relationships & Indexing

• Foreign keys are strictly enforced for referential integrity.
• Unique constraints on names, versions, and identifiers where necessary.
• Indexes on all major lookup paths (by cluster, status, time, etc.).
• Data partitioning: Large tables (billing_records, cost_attributions, audit_logs) partitioned by month for performance and retention.
• Sharding: Phase 2+ (if >10K clusters).
• Backup/recovery: Nightly full + hourly incremental backups, PITR enabled.

ER Diagram

erDiagram
 clusters ||--o{ cluster_agents : "has"
 clusters ||--o{ cluster_snapshots : "has"
 clusters ||--o{ workload_promotions : "has"
 clusters ||--o{ helm_releases : "has"
 clusters ||--o{ cost_attributions : "has"
 clusters ||--o{ remediation_rules : "has"
 cluster_agents ||--|| clusters : "belongs to"
 workload_promotions ||--o{ workload_diffs : "has"
 workload_promotions ||--|| promotion_costs : "cost"
 workload_promotions ||--|| promotion_risks : "risk"
 workload_promotions ||--|| llm_explanations : "explanation"
 workload_promotions ||--|| git_commits : "commit"
 workload_promotions ||--|| git_pull_requests : "pr"
 workload_promotions ||--|| users : "requested by"
 helm_releases ||--|| helm_charts : "from"
 helm_releases ||--|| clusters : "in"
 billing_accounts ||--o{ billing_imports : "has"
 billing_accounts ||--o{ billing_records : "has"
 billing_records ||--o{ cost_attributions : "maps"
 cost_attributions ||--|| clusters : "for"
 remediation_rules ||--o{ remediation_actions : "triggers"
 remediation_actions ||--|| remediation_approvals : "approval"
 remediation_actions ||--|| audit_logs : "logs"
 remediation_approvals ||--|| users : "by"
 users ||--o{ audit_logs : "performs"
 users ||--o{ rbac_role_bindings : "has"
 rbac_roles ||--o{ rbac_role_bindings : "bound"

5. API DESIGN

API Versioning:

All endpoints are under /api/v1/. Future breaking changes will use /api/v2/.

Authentication:

• OIDC JWT Bearer tokens (Auth0, Cognito, Azure AD).
• All endpoints require Authorization header.
• RBAC enforced per endpoint/cluster.

Rate Limiting:

• Default: 100 req/min/user on write endpoints, 1000 req/min/user on read endpoints.
• Customizable per org.

Error Handling:

• 400 Bad Request (validation), 401 Unauthorized, 403 Forbidden, 404 Not Found, 409 Conflict, 422 Unprocessable, 429 Too Many Requests, 500 Internal Error.
• All errors include {error: string, details?: object}.

Endpoints (Feature-to-Endpoint Mapping, 36 endpoints)

Clusters & Agents

1. POST /api/v1/clusters

• Create new cluster record (register agent)
• Request: {name, cloud_provider, region, metadata}
• Response: {id, ...}

2. GET /api/v1/clusters

• List clusters
• Response: [ {id, name, cloud_provider, region, status, agent_status, ...} ]

3. GET /api/v1/clusters/:cluster_id

• Get cluster details

4. GET /api/v1/clusters/:cluster_id/agents

• List agents for cluster

5. GET /api/v1/clusters/:cluster_id/snapshots

• List cluster snapshots (paginated)

6. POST /api/v1/clusters/:cluster_id/snapshots

• Agent submits snapshot (secured, agent JWT)

7. GET /api/v1/clusters/:cluster_id/agent-status

• Get current agent heartbeat/status

Workload Promotion Preview

8. POST /api/v1/promotions/preview

• Preview workload promotion
• Request: {cluster_id, workload_name, source_namespace, target_namespace}
• Response: {promotion_id, diff, estimated_cost, risk_level, explanation}

9. POST /api/v1/promotions/:promotion_id/approve

• Approve promotion (creates Git PR)

10. POST /api/v1/promotions/:promotion_id/reject

• Reject promotion

11. GET /api/v1/promotions

• List promotions (filter by cluster, status)

12. GET /api/v1/promotions/:promotion_id

• Get promotion details (diff, cost, risk, explanation)

13. GET /api/v1/promotions/:promotion_id/diff

• Get diff for promotion

14. GET /api/v1/promotions/:promotion_id/cost

• Get cost preview

15. GET /api/v1/promotions/:promotion_id/risk

• Get risk assessment

16. GET /api/v1/promotions/:promotion_id/explanation

• Get LLM explanation

Helm Management

17. GET /api/v1/clusters/:cluster_id/helm/releases

• List Helm releases in cluster

18. POST /api/v1/clusters/:cluster_id/helm/releases

• Promote new Helm release (via Git PR)

19. GET /api/v1/helm/charts

• List available Helm charts

20. POST /api/v1/helm/charts

• Add new Helm chart

21. GET /api/v1/helm/charts/:chart_id

• Get chart details

GitOps Orchestration

22. POST /api/v1/git/repos

• Register Git repository

23. GET /api/v1/git/repos

• List Git repositories

24. GET /api/v1/git/repos/:repo_id/commits

• List commits

25. GET /api/v1/git/repos/:repo_id/pull-requests

• List PRs

26. GET /api/v1/git/pull-requests/:pr_id

• Get PR details

Billing & FinOps

27. POST /api/v1/billing/accounts

• Register cloud billing account

28. GET /api/v1/billing/accounts

• List billing accounts

29. POST /api/v1/billing/imports

• Trigger billing data import

30. GET /api/v1/billing/records

• List billing records (filterable)

31. GET /api/v1/billing/attributions

• List cost attributions (by cluster/workload)

32. GET /api/v1/billing/anomalies

• List detected cost anomalies

Auto-Remediation

33. POST /api/v1/remediation/rules

• Create remediation rule

34. GET /api/v1/remediation/rules

• List remediation rules

35. POST /api/v1/remediation/actions/:action_id/approve

• Approve remediation action (creates Git PR)

36. GET /api/v1/remediation/actions

• List remediation actions (filterable)

Audit & RBAC

37. GET /api/v1/audit/logs

• List audit logs (filterable)

38. GET /api/v1/users/me

• Get current user profile

39. GET /api/v1/rbac/roles

• List roles

40. POST /api/v1/rbac/roles

• Create role

41. POST /api/v1/rbac/bindings

• Assign role to user/cluster

Sample Endpoint Definition

POST /api/v1/promotions/preview

• Request:

• Response:

• Auth: Bearer JWT (OIDC)
• Rate Limit: 100 req/min
• Status: 201 Created, 400 Bad Request, 401 Unauthorized, 422 Unprocessable

API Structure Diagram

flowchart TD
 A["API Gateway"]
 B["/clusters"]
 C["/promotions"]
 D["/helm"]
 E["/git"]
 F["/billing"]
 G["/remediation"]
 H["/audit"]
 I["/users"]
 J["/rbac"]

 A-->|CRUD|B
 A-->|Preview/Approve|C
 A-->|Manage|D
 A-->|GitOps|E
 A-->|FinOps|F
 A-->|Auto-Remediate|G
 A-->|Audit|H
 A-->|Profile|I
 A-->|RBAC|J

6. COMPONENT ARCHITECTURE

Feature-Specific Components

1. Kubernetes Workload Promotion Preview

• PromotionPreviewService: Computes workload diffs, invokes CostEngine and RiskAnalyzer, generates preview.
• CostEngine: Estimates cost delta using cluster state and billing data.
• RiskAnalyzer: Analyzes risk (quotas, policies, drift).
• LLMExplanationService: Calls LLM API w/ promotion metadata for explanation.
• DiffVisualizer (UI): Renders resource diff/cost/risk/explanation.
• PromotionAPI: Orchestrates preview, approval, and audit logging.

2. Lightweight Read-Only Kubernetes Agent

• AgentCollector: Runs in cluster, collects resource state, sends signed snapshot via gRPC/TLS.
• AgentHeartbeat: Sends liveness pings to SaaS.
• AgentInstaller: Helm chart, versioned, RBAC-scoped.

3. GitOps Orchestration

• GitOpsOrchestrator: Creates/updates Git commits/PRs for promotions/remediations.
• GitEventListener: Polls/webhooks for PR merges, triggers status updates.
• AuditLogger: Appends immutable audit log entries.

4. LLM Explanation Engine

• LLMExplanationService: Prepares prompt, calls LLM API, stores result.
• PromptBuilder: Formats structured data for LLM.
• ExplanationCache: Redis-backed cache for deduplication.

5. Cluster Management UI

• ClusterList (UI): Lists clusters, agent status, metadata.
• ClusterDetail (UI): Agent heartbeat, snapshots, workload inventory.

6. Helm Management UI

• HelmReleaseList (UI): Lists Helm releases per cluster.
• HelmReleaseManager: Promotes/rolls back releases (via GitOps).

7. Deep FinOps Billing Reconciliation

• BillingIngestor: Imports billing data from cloud APIs.
• CostAttributionEngine: Maps billing records to K8s workloads.
• AnomalyDetector: Flags post-promotion cost spikes.

8. Auto-Remediation with Human Approval

• RemediationRuleEngine: Detects triggers, proposes actions.
• ApprovalWorkflow (UI/API): Surfaces actions for human approval.
• RemediationExecutor: On approval, creates Git PR (no direct mutation).
• AuditLogger: Logs all actions/approvals.

9. Integration: Cloud Provider Billing APIs

• BillingAPIClient: Secure fetch from AWS/GCP/Azure billing.

10. Infrastructure: Hybrid Delivery Model

• ControlPlaneDeployer: Helm packaging/versioning, SaaS hosting, TLS, OIDC.

Component Data Flow Diagram (Promotion Preview)

flowchart TD
 A["Operator UI"]
 B["PromotionAPI"]
 C["PromotionPreviewService"]
 D["AgentCollector"]
 E["CostEngine"]
 F["RiskAnalyzer"]
 G["LLMExplanationService"]
 H["GitOpsOrchestrator"]
 I["AuditLogger"]

 A-->|Preview request|B
 B-->|Get cluster state|D
 B-->|Compute diff|C
 C-->|Estimate cost|E
 C-->|Assess risk|F
 C-->|Generate explanation|G
 C-->|Aggregate results|B
 B-->|Show preview|A
 A-->|Approve|B
 B-->|Create Git PR|H
 H-->|Log|I

7. INTEGRATION ARCHITECTURE

1. Git Provider (GitHub, GitLab, Bitbucket, Azure DevOps)

• API Integration: OAuth2 app, PAT/installation tokens stored encrypted.
• Protocols: HTTPS (REST/gRPC), webhook/polling for PR/commit events.
• Data Format: JSON.
• Error Handling: Retry w/ exponential backoff, alert on auth errors.
• Security: Least-privilege repo/token scopes.
• Audit: All actions logged with user/context.

2. Cloud Provider Billing APIs (AWS, GCP, Azure)

• API Integration: Secure API clients per provider, credentials encrypted.
• Protocols: HTTPS (REST, JSON/CSV).
• Data Normalization: Map provider line items to internal schema.
• Error Handling: Retry, notify on quota/auth failures.
• Security: Read-only billing scopes.

3. LLM Provider (OpenAI/Azure OpenAI)

• API Integration: HTTPS, API key in secret store.
• Data Format: JSON.
• Prompt Engineering: All prompts sanitized, no customer secrets.
• Error Handling: Circuit breaker, fallback message on LLM outage.
• Rate Limiting: Respect provider limits, cache repeated prompts.

4. Kubernetes API (per-cluster)

• Integration: Read-only, TLS, service account scoped by RBAC.
• Protocols: HTTPS w/ mTLS agent-server.
• Security: No write permissions, audit logs for access.

5. Helm SDK

• Integration: Go SDK, version pin 3.15.
• Protocols: Local API, no network exposure.

8. SECURITY CONSIDERATIONS

• Authentication: OIDC JWT, SSO via Auth0/Cognito/Azure AD.
• Authorization: Strict RBAC per user/cluster/namespace, enforced at API and DB layer.
• Agent Security: Helm-installed, read-only RBAC, mTLS, agent key rotation.
• SOC2 Compliance: Immutable audit logs, evidence collection (Drata/Vanta), separation of duties.
• Data Encryption: At-rest (AES-256) and in-transit (TLS 1.3).
• Secrets: KMS-encrypted, never logged or sent to LLM.
• Auditability: All actions, approvals, and GitOps events are logged, write-once.
• Threat Model: No direct cluster mutation, agent is read-only, all promotions/remediations are Git-driven.
• Security Monitoring: Prometheus/Sentry for anomaly detection, alerting on suspicious activity.
• Hardening: Minimal agent footprint, no privileged containers, regular dependency scanning.

9. SCALABILITY PLAN

• Horizontal Scaling: All SaaS control plane services are stateless, scale via Kubernetes HPA.
• Load Balancing: API Gateway (Envoy/NGINX) with sticky sessions for UI, stateless API.
• Caching: Redis 7.2 for session, LLM explanation, cost preview caching.
• Partitioning: Large tables (audit, billing, snapshots) partitioned by month.
• Auto-Scaling: HPA for API, cost engine, LLM, GitOps orchestrator.
• Performance Optimization: Async processing for billing and LLM, query/index tuning.
• CDN: Phase 2+ for UI assets.
• Agent Fleet: Per-cluster agent can scale to 10K+ clusters with stateless SaaS.

10. DEPLOYMENT STRATEGY

• Cloud Infrastructure: Control plane on AWS (EKS 1.36.1), Postgres 16 (RDS), Redis 7.2 (Elasticache), S3 for object storage.
• Containerization: All services use Docker 26 images, Helm 3.15 for agent install.
• CI/CD: GitHub Actions for build/test/deploy; ArgoCD 2.11 for GitOps orchestration.
• Monitoring & Observability: Prometheus 2.54 (metrics), Grafana 11 (dashboards), Sentry 24 (errors), Loki (logs).
• DevOps: IaC (Terraform, Phase 2+), Helm for agent.
• Disaster Recovery: PITR for Postgres, daily Redis backup, multi-AZ RDS, Helm agent stateless (can be reinstalled).

10a. PHASE 1 DEPLOYMENT — Minimum Viable Infrastructure

Platform: Railway.app Rationale: Fastest path to MVP, single managed service, Postgres 16 and Redis 7.2 available, Docker-based deploy, secure env var management.

Deployment Topology:

• Control plane: single Docker Compose service (Go API, React UI, LLM integration, cost engine, GitOps orchestrator, all in one container for MVP)
• Database: Railway-managed PostgreSQL 16
• Cache: Railway-managed Redis 7.2
• Secrets: Railway env vars (API keys, DB creds)
• Agent: Helm chart, installed in test K8s cluster, points to Railway SaaS URL (TLS)
• LLM: OpenAI API key in Railway secrets

Docker Compose Essentials:

• docker-compose.yml for API/UI, with env vars for DB/Redis/LLM/Git tokens.

Deferred (Phase 2+):

• Kubernetes/EKS, multi-node, CDN, auto-scaling, multi-region, ArgoCD, Prometheus, full IaC, WAF, dedicated Redis cluster.

Estimated Setup Time: 4–8 hours for solo developer with basic Railway and Helm experience.

Deployment Topology Diagram

flowchart TD
 A["Operator Browser"]
 B["Railway Web App (API/UI/LLM/Cost Engine)"]
 C["Railway Postgres 16"]
 D["Railway Redis 7.2"]
 E["Cluster Agent (Helm, Helm 3.15, Go 1.22)"]
 F["OpenAI API"]
 G["GitHub/GitLab API"]
 H["Cloud Billing API"]

 A-->|HTTPS|B
 B-->|DB|C
 B-->|Cache|D
 B-->|LLM|F
 B-->|GitOps|G
 B-->|Billing|H
 E-->|TLS|B

11. TRADE-OFFS

• Go vs. Node.js/Python: Go chosen for security, performance, and Kubernetes-native development. Node.js/Python would accelerate MVP, but Go is more maintainable for hybrid delivery/SOC2.
• Railway for Phase 1: Not enterprise-grade, but fast for MVP. Phase 2+ migrates to AWS/GCP with full IaC.
• Monorepo for MVP: Single codebase for all control plane services simplifies initial deploy; microservices split in Phase 2.
• LLM API vs. Self-Hosted: OpenAI API is fastest to value, but may add latency/cost. Self-hosting considered for Phase 2+.
• Agent Read-only: Sacrifices some operational flexibility, but maximizes trust, security, and auditability.
• No direct cluster mutation: All changes via GitOps adds latency but guarantees audit trail and intent.

12. ALTERNATIVES CONSIDERED

• Full-Stack TypeScript (Node.js/NestJS): Faster for MVP, but less mature for K8s/agent development, less optimal for hybrid delivery.
• Rust Backend: Maximum security, but increases hiring/training complexity and slows initial delivery.
• Monolith SaaS: Rejected in favor of hybrid; hybrid model required by large enterprises for trust/compliance.
• Direct Cluster Mutation: Rejected for security/audit reasons—read-only agent + GitOps is non-negotiable.
• Self-Hosted LLM: Considered for data privacy, but deferred due to operational overhead and MVP velocity.
• Multi-cloud SaaS-only: Not chosen; hybrid delivery is a core requirement for trust and compliance.

Validation Checklist:

• ✅ All features mapped to specific tables, endpoints, components
• ✅ No generic placeholders (all schemas and APIs are complete)
• ✅ Technology stack and versions derived from latest evidence
• ✅ Minimum viable infrastructure and deployment path provided
• ✅ Security, scalability, and production-readiness addressed throughout
• ✅ Diagrams provided for all major architecture aspects

This architecture document provides a comprehensive, actionable blueprint for building KubeCost Preview, aligned with enterprise, security, and hybrid delivery requirements.

Product Requirements Document (PRD)

Kubernetes Promotion Cost Preview Tool

1. Executive Summary

The Kubernetes Promotion Cost Preview Tool is a Git-first, security-first Kubernetes control plane designed for Platform Engineers, SREs, and Engineering Managers at mid-to-large enterprises operating Kubernetes at scale on AWS, GCP, or Azure. The product addresses three compounding pain points: environment drift between staging and production, risky workload promotions without operational or cost impact preview, and unpredictable cloud cost spikes post-deployment. By integrating deployment intent, operational change preview, and cost impact analysis into a single, auditable workflow, the platform delivers actionable insights and plain-English explanations powered by LLMs. The hybrid delivery model (SaaS control plane + per-cluster read-only agent) ensures enterprise-grade security, SOC2 compliance, and rapid onboarding, with a goal of delivering value within 7 days of signup. Success is measured by completed promotion previews, not just signups.

2. Problem Statement

Modern platform teams running Kubernetes at scale face three critical, interrelated challenges:

1. Environment Drift: Inconsistencies between staging and production environments lead to unpredictable production behavior and incidents.
2. Risky Promotions: Lack of actionable, integrated previews of operational and cost impact before promoting workloads results in outages and unexpected spend.
3. Cloud Cost Spikes: Cost overruns are often detected only after deployment, making remediation reactive and expensive.

Target Audience: Platform Engineers, SREs, and Engineering Managers at mid-to-large enterprises (100+ engineers, $1M+ annual cloud spend) with mature DevOps practices, operating Kubernetes on AWS, GCP, or Azure. These organizations often have compliance mandates (SOC2, zero-trust) and a history of production incidents or cost overruns.

Current Workarounds: Fragmented toolchains—GitOps tools (ArgoCD, Flux) for deployments, siloed cost dashboards (CloudHealth, Cloudability), and manual pre-deployment reviews—fail to provide an integrated, actionable preview of both operational and financial impact.

3. Solution Overview

The solution is a Git-first, security-first Kubernetes control plane that unifies deployment intent, operational change preview, and cost impact analysis into a single workflow. Key innovations include:

• Read-only, per-cluster agent (deployed via Helm) that gathers cluster state without direct mutation.
• All promotions orchestrated through Git (GitHub, GitLab, Bitbucket), ensuring full auditability and traceability.
• LLM-powered explanation engine that generates plain-English risk and cost reasoning for each promotion.
• Hybrid delivery model: SaaS control plane (UI, APIs, orchestration, cost engine) + per-cluster agent.
• Enterprise-grade security: Strict read-only boundaries, scoped RBAC, SOC2 compliance, and zero direct cluster mutation.
• Integrated cluster and Helm management UI, deep FinOps billing reconciliation, and auto-remediation with human approval.

Unique Value: The only platform to connect deployment intent, operational change, and cost impact in a single, auditable workflow with actionable, plain-English explanations and strict security boundaries.

4. Stakeholder Analysis

5. User Personas

Persona 1: Alex Kim – Platform Engineer

• Demographics: Age 32, San Francisco, CA; works at a SaaS company with 300+ engineers; Kubernetes on AWS.
• Goals/Objectives:
  • Ensure safe, predictable workload promotions.
  • Minimize production incidents due to environment drift.
  • Reduce manual effort in deployment reviews.
• Pain Points:
  • Fragmented tools for deployments and cost visibility.
  • No integrated preview of operational/cost impact.
  • Time-consuming manual reviews and post-incident firefighting.
• Use Cases:
  • Initiate workload promotion, review operational/cost preview, approve or reject promotion.
  • Investigate environment drift between staging and production.
  • Use audit logs for post-incident analysis.
• Technology Comfort Level: Advanced (Kubernetes, GitOps, Helm, cloud APIs).
• Behavioral Patterns: Proactive in automation, prefers self-service tools, values auditability and transparency.

Persona 2: Priya Singh – Site Reliability Engineer (SRE)

• Demographics: Age 38, London, UK; works at a fintech with 500+ engineers; Kubernetes on GCP.
• Goals/Objectives:
  • Maintain production stability and uptime.
  • Identify and mitigate risky promotions before they reach production.
  • Ensure compliance with SOC2 and internal audit requirements.
• Pain Points:
  • Outages caused by unanticipated changes.
  • Lack of actionable, plain-English risk explanations.
  • Difficulty correlating deployment changes with cost spikes.
• Use Cases:
  • Review promotion previews for risk/cost.
  • Approve or reject auto-remediation PRs.
  • Use audit trails for compliance reporting.
• Technology Comfort Level: Expert (Kubernetes, cloud security, GitOps, monitoring).
• Behavioral Patterns: Risk-averse, detail-oriented, collaborates closely with platform and security teams.

Persona 3: Jordan Lee – Engineering Manager

• Demographics: Age 45, New York, NY; manages a cloud-native team at a healthcare enterprise (250 engineers); Kubernetes on Azure.
• Goals/Objectives:
  • Deliver features rapidly without compromising production safety.
  • Control cloud spend and prevent cost overruns.
  • Ensure all deployments are auditable and compliant.
• Pain Points:
  • Lack of integrated cost impact preview before deployment.
  • Siloed cost dashboards not actionable for engineers.
  • Compliance and audit reporting is manual and error-prone.
• Use Cases:
  • Monitor promotion activity and cost trends.
  • Review audit logs for compliance.
  • Oversee onboarding of new clusters and teams.
• Technology Comfort Level: Intermediate (Kubernetes concepts, cloud billing, compliance tools).
• Behavioral Patterns: Data-driven, values transparency, responsible for cross-team alignment.

Persona 4: Maria Rodriguez – FinOps Analyst

• Demographics: Age 29, Austin, TX; works at a SaaS scale-up (120 engineers); Kubernetes on AWS.
• Goals/Objectives:
  • Reconcile cloud billing with actual cluster usage.
  • Detect and prevent cost anomalies before they escalate.
  • Provide actionable cost insights to engineering teams.
• Pain Points:
  • Delayed visibility into cost spikes post-deployment.
  • Difficulty mapping workloads to cost centers.
  • Manual, error-prone billing reconciliation.
• Use Cases:
  • Analyze cost previews for upcoming promotions.
  • Investigate cost anomalies with operational context.
  • Generate reports for finance and engineering leadership.
• Technology Comfort Level: Intermediate (cloud billing APIs, reporting tools).
• Behavioral Patterns: Analytical, collaborates with engineering and finance, values automation.

Persona 5: Samir Patel – Security & Compliance Lead

• Demographics: Age 41, Chicago, IL; works at a regulated healthcare provider (400 engineers); Kubernetes on GCP.
• Goals/Objectives:
  • Ensure SOC2 compliance and auditability of all deployments.
  • Enforce strict RBAC and read-only boundaries.
  • Minimize operational risk from unauthorized changes.
• Pain Points:
  • Tools that mutate clusters directly, increasing risk.
  • Lack of centralized, auditable promotion workflows.
  • Manual compliance evidence gathering.
• Use Cases:
  • Review audit logs for compliance checks.
  • Validate RBAC and agent permissions.
  • Oversee security posture of deployment workflows.
• Technology Comfort Level: Advanced (Kubernetes RBAC, compliance frameworks, audit tools).
• Behavioral Patterns: Security-first, process-driven, collaborates with SREs and platform teams.

6. Technical Requirements

Reference: Architecture Document (KubeCost Preview — Enterprise-Grade Technical Architecture)

Technology Stack

• Frontend: React 19.x (Operator UI)
• Backend: Go 1.22 (API Gateway, services), gRPC + REST APIs
• Auth: OIDC (OpenID Connect)
• Database: PostgreSQL 16
• Cache: Redis 7.2
• Per-Cluster Agent: Go (read-only), Helm 3.15
• LLM Integration: OpenAI/Azure OpenAI or self-hosted LLM
• Cloud Billing APIs: AWS, GCP, Azure
• GitOps Integration: GitHub, GitLab, Bitbucket APIs
• Kubernetes API: v1.36.1+
• Infrastructure: SOC2-compliant, multi-tenant SaaS control plane

Component Alignment

• Frontend: React 19 UI for cluster, Helm, workload, and audit management.
• Backend Services: API Gateway, Promotion Preview Service, Cost Engine, LLM Explanation Engine, GitOps Orchestrator, Audit Log Service, Cluster Management, Helm Management, Billing Reconciliation, Auto-Remediation.
• Agent: Helm-installed, cluster-scoped, read-only, RBAC-enforced.
• Integration Points: Git providers, cloud billing APIs, LLM provider, Kubernetes APIs, Helm APIs/SDK.

Categorized Technical Requirements

Frontend:

• React 19.x, TypeScript, Material UI.
• Secure OIDC-based authentication.
• Real-time updates via WebSockets/gRPC streaming.
• Responsive design for operator workflows.

Backend:

• Go 1.22 microservices.
• API Gateway exposes gRPC and REST endpoints.
• Promotion Preview Service computes operational/cost diffs.
• Cost Engine aggregates cluster/billing data.
• LLM Explanation Engine for plain-English output.
• GitOps Orchestrator for Git-based promotions.
• Audit Log Service for immutable event logging.

Infrastructure:

• Multi-tenant SaaS, SOC2-compliant.
• PostgreSQL 16 for persistent storage.
• Redis 7.2 for caching and job queues.
• Secure, encrypted data at rest and in transit.

Security:

• OIDC authentication, JWT session management.
• Scoped RBAC enforced at UI, API, and agent levels.
• Read-only agent (no cluster mutation).
• Full audit logging of all actions.

Integration:

• GitHub/GitLab/Bitbucket APIs for GitOps.
• AWS/GCP/Azure billing APIs for cost data.
• LLM provider API for explanations.
• Kubernetes API (read-only), Helm SDK.

Configuration & Performance:

• Agent sync interval: configurable (default 60s).
• Promotion preview SLA: <5s for 95th percentile.
• Support for 1000+ clusters per tenant.
• Database schema: see Data Architecture section.

7. Non-Functional Requirements

• Performance: Promotion preview response time <5s (95th percentile); UI loads <2s.
• Scalability: Support 1000+ clusters/tenant; 10,000+ concurrent users.
• Reliability: 99.9% uptime SLA; automatic failover for control plane services.
• Availability: Multi-AZ deployment; stateless services with rolling updates.
• Security: SOC2 compliance, OIDC authentication, RBAC, encrypted data at rest/in transit.
• Privacy: No PII stored; all data encrypted; agent transmits only metadata.
• Compliance: SOC2, zero-trust, audit trail requirements.
• Maintainability: Modular microservices, automated CI/CD, versioned APIs.
• Observability: Centralized logging, metrics, and distributed tracing.

8. Success Metrics & KPIs

9. Risks

10. Assumptions

• Target users have existing Kubernetes clusters on AWS, GCP, or Azure.
• Organizations have mature DevOps practices and use GitOps workflows.
• SOC2 compliance is a non-negotiable requirement.
• Users have access to cloud billing APIs and Git provider APIs.
• No direct cluster mutation is permitted; all changes flow through Git.
• LLM integration is available via OpenAI/Azure OpenAI or self-hosted.
• All clusters can install Helm-based agents with read-only RBAC.
• No PII or sensitive data is transmitted from clusters.
• Users are comfortable with web-based UIs and Git-based workflows.
• Enterprise customers require audit trails and RBAC.

11. Compliance & Regulatory Requirements

• SOC2 Type II: All control plane components and workflows must meet SOC2 requirements for security, availability, processing integrity, confidentiality, and privacy.
• Zero-Trust Architecture: No direct cluster mutation; all actions are auditable and require explicit approval.
• Audit Trails: Immutable, exportable logs for all user and system actions.
• Data Residency: Support for regional data storage as required by enterprise customers.
• Industry Regulations: Support for regulated industries (finance, healthcare, SaaS) with additional controls as needed.

12. Security & Privacy Requirements

• Authentication: OIDC (OpenID Connect) with JWT tokens; SSO integration.
• Authorization: Scoped RBAC at user, team, and cluster levels.
• Agent Security: Read-only, RBAC-enforced, no write permissions.
• Data Encryption: TLS 1.3 for all data in transit; AES-256 for data at rest.
• Audit Logging: Immutable, tamper-evident logs for all actions.
• LLM Privacy: No sensitive data sent to LLM provider; redact cluster names, secrets.
• Agent Communication: Outbound-only, mutual TLS, signed payloads.
• Secrets Management: No secrets stored in control plane; use cloud-native secret stores.
• Compliance: SOC2 controls, regular penetration testing, security reviews.

13. Integration Requirements

• Git Providers: GitHub, GitLab, Bitbucket (OAuth2, webhooks, PR APIs).
• Cloud Billing APIs: AWS Cost Explorer, GCP Billing, Azure Cost Management.
• LLM Provider: OpenAI/Azure OpenAI (API key management, usage monitoring).
• Kubernetes API: v1.36.1+ (read-only access).
• Helm SDK/API: For workload and release management.
• SIEM/Logging: Optional integration with Splunk, Datadog, or ELK for audit logs.
• Notification Channels: Slack, email, webhook for promotion events and alerts.

14. Data Architecture

Reference: Architecture's Database Schema

Data Models

• Clusters (clusters): Cluster metadata, connection info, agent status.
• Workloads (workloads): Workload definitions, versions, promotion history.
• Promotions (promotions): Promotion requests, preview results, approval status.
• Cost Previews (cost_previews): Estimated cost impact, resource deltas.
• Risk Explanations (risk_explanations): LLM-generated plain-English outputs.
• Users (users): User profiles, RBAC roles, authentication info.
• Audit Logs (audit_logs): Immutable logs of all actions/events.
• Billing Data (billing_data): Cloud billing records, reconciliation status.

Relationships

• clusters 1:N workloads
• workloads 1:N promotions
• promotions 1:1 cost_previews
• promotions 1:1 risk_explanations
• users N:M clusters (via RBAC)
• promotions 1:N

Storage Strategies

• PostgreSQL 16: Primary data store for all entities.
• Redis 7.2: Caching, job queues, LLM prompt/response caching.
• Immutable audit logs: Write-once, append-only.

Data Processing & Flow

1. Agent syncs cluster state to control plane (read-only).
2. User initiates promotion; control plane computes diff/cost preview.
3. LLM generates risk/cost explanations.
4. On approval, GitOps Orchestrator commits to Git.
5. Billing engine ingests and reconciles cloud billing data.
6. Audit logs record all actions.

Data Flow Diagram

(See Architecture's ER diagram for full detail.)

15. API Specifications

Reference: Architecture's API Design

API Versioning

• All endpoints are versioned under /api/v1/.

Authentication

• OIDC JWT required for all endpoints.
• RBAC enforced per endpoint.

Rate Limiting

• 1000 requests/min/user (configurable per tenant).

Error Handling

• Standardized error codes and messages (JSON).

Feature-to-Endpoint Mapping

Example Endpoint Definitions

POST /api/v1/promotions/preview

PATCH /api/v1/promotions/:id/approve

GET /api/v1/costs/anomalies

Authentication/Authorization:

• All endpoints require OIDC JWT in Authorization header.
• RBAC enforced on all resource access.

Rate Limiting:

• 1000 requests/min/user (configurable per tenant).

Error Handling:

• Standardized error responses:

16. Testing Strategy

• Unit Testing: 90%+ coverage for all backend services and agent code.
• Integration Testing: End-to-end tests for agent → control plane → GitOps → billing data flows.
• E2E Testing: Automated UI tests (Cypress) for core workflows (promotion preview, approval, audit logs).
• Performance Testing: Load tests for 1000+ clusters, 10,000+ concurrent users.
• Security Testing: Regular penetration tests, static code analysis, RBAC/permission tests.
• Compliance Testing: SOC2 control validation, audit log integrity checks.
• LLM Output Testing: Validation of explanation accuracy, redaction of sensitive data.

17. Deployment Strategy

• Phased Rollout: Start with pilot customers, expand to general availability.
• Blue-Green Deployments: For SaaS control plane updates.
• Agent Upgrades: Helm chart versioning, backward compatibility, canary releases.
• Rollback Procedures: Automated rollback on deployment failure; agent rollback via Helm.
• Disaster Recovery: Daily backups, multi-AZ failover, RPO <1 hour, RTO <2 hours.
• Onboarding: Guided onboarding for agent installation and Git/cloud integration.

18. Monitoring & Observability

• Metrics: Promotion preview latency, agent sync status, cost anomaly detection, API error rates.
• Alerts: Promotion failures, agent offline, cost spikes, audit log tampering.
• Dashboards: Real-time cluster health, promotion activity, cost trends, compliance status.
• Logging: Centralized, structured logs for all services; immutable audit logs.
• Tracing: Distributed tracing for promotion workflows and API calls.

19. Timeline & Phases

20. Resource Requirements

• Team Composition:
  • Product Owner (1)
  • Engineering Manager (1)
  • Frontend Engineers (2-3, React/TypeScript)
  • Backend Engineers (4-5, Go, gRPC, PostgreSQL)
  • DevOps/SRE (2, Kubernetes, cloud, Helm)
  • Security/Compliance Lead (1)
  • QA/Test Engineers (2)
  • FinOps Analyst (1)
  • LLM/AI Engineer (1)
  • UX Designer (1)
  • Technical Writer (1)
• Skills Needed: Kubernetes, GitOps, cloud APIs, Go, React, Helm, SOC2, RBAC, LLM integration, FinOps, security, compliance.
• Budget Considerations: Cloud infrastructure, LLM API costs, SOC2 audit fees, onboarding/support resources.

21. Change Management Plan

• User Adoption: Guided onboarding, in-app tutorials, documentation, webinars.
• Training: Role-based training for platform engineers, SREs, managers.
• Communication: Regular release notes, roadmap updates, customer advisory board.
• Feedback Loops: In-app feedback, quarterly NPS surveys, user interviews.
• Support: Tiered support (standard, premium), dedicated onboarding for enterprise customers.
• Documentation: Comprehensive user guides, API docs, compliance playbooks.

End of PRD

Note: User stories will be generated in the next phase based on this PRD. All requirements, endpoints, and technical details are aligned with the validated evaluation intelligence and the canonical architecture. No generic content remains; all sections are tailored to the specific needs, constraints, and context provided.

User Stories & Epics

Epic: Time-to-First-Value within 7 Days

This epic focuses on delivering a simple onboarding process, quick agent installation, and fast initial data sync and promotion previews to enable users to achieve meaningful value within 7 days of signup.

Optimize initial data sync and promotion preview generation

As the system, I want the initial data sync from agent and promotion preview generation to be fast, so that users see value quickly after onboarding

Priority: high

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4), Integrate cost estimation engine for promotion previews (ae034446-cc87-4de4-96e0-eb8d6e7eb2b1)

Acceptance Criteria:

• Functional: Initial agent snapshot sync completes within 5 minutes
• Functional: First promotion preview response time under 5 seconds
• Technical: Data ingestion pipelines optimized for low latency
• Technical: Caching used to speed up repeated preview requests
• Technical: Monitoring tracks initial sync and preview performance

Story Points: 5

Estimated Effort: 8 hours

Enable quick agent installation via Helm with minimal configuration

As a platform engineer, I want to install the Kubernetes agent quickly via Helm with minimal configuration, so that initial setup time is reduced

Priority: high

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Create Helm chart documentation and installation guides for agent (df4842d1-446d-4ba3-bae6-71af61a90947)

Acceptance Criteria:

• Functional: Helm chart supports default values for quick install
• Functional: Installation completes within 10 minutes
• Technical: Helm chart includes pre-install validation hooks
• Technical: Installation errors reported clearly in UI and CLI
• Technical: Installation process documented with examples

Story Points: 3

Estimated Effort: 5 hours

Implement streamlined onboarding process with guided steps

As a new platform engineer, I want a simple, guided onboarding process, so that I can start using the platform quickly and effectively

Priority: high

Dependencies: Implement cluster list and detail views in UI (e18ee699-6beb-4b8c-b80e-377148738b5c), Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Integrate with Git providers for promotion workflows (71cb6fa4-b794-4015-8fba-b5f6ba179de4)

Acceptance Criteria:

• Functional: Onboarding wizard guides users through cluster registration, agent installation, and Git integration
• Functional: Progress saved and resumable
• Technical: Onboarding UI implemented with React 19
• Technical: Onboarding steps integrated with backend APIs
• Technical: Onboarding completion tracked for analytics

Story Points: 5

Estimated Effort: 8 hours

Epic: Reliability and Availability

This epic ensures the platform achieves high availability, fault tolerance, and reliability to minimize downtime and maintain continuous operation.

Implement monitoring and alerting on system health

As an SRE, I want monitoring and alerting configured for control plane and agent health metrics, so that issues are detected and resolved promptly

Priority: high

Dependencies: Deploy SaaS control plane with high availability (4563269b-329a-416e-a3e1-c9f5633a07b3), Implement agent heartbeat and status reporting (b60282f6-53c3-4606-97c6-a3df7e241214), Detect and alert on cost anomalies post-deployment (0ad6122f-4c93-4482-a7c1-c29be83d030f)

Acceptance Criteria:

• Functional: Metrics collected for API latency, error rates, agent sync status, and cost anomaly detection
• Functional: Alerts configured for promotion failures, agent offline, and cost spikes
• Technical: Use Prometheus 2.54 and Grafana 11 for metrics and dashboards
• Technical: Alerts integrated with Slack and email notification channels
• Technical: Monitoring covers all critical microservices and infrastructure

Story Points: 5

Estimated Effort: 8 hours

Implement graceful agent reconnection and failover

As the system, I want the Kubernetes agent to reconnect gracefully after network interruptions and failover scenarios, so that data sync is reliable

Priority: high

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Implement secure communication channel between agent and control plane (dac1050d-6dce-4f33-984e-d7ce648fd7be), Implement agent heartbeat and status reporting (b60282f6-53c3-4606-97c6-a3df7e241214)

Acceptance Criteria:

• Functional: Agent retries connection with exponential backoff on failures
• Functional: Agent buffers data locally during disconnections
• Technical: Agent resumes sync without data loss after reconnect
• Technical: Failover scenarios tested in staging environments
• Technical: Agent status reflected accurately in control plane

Story Points: 3

Estimated Effort: 5 hours

Implement redundant control plane components with failover

As the DevOps team, I want control plane services deployed redundantly with failover capabilities, so that the platform meets 99.9% uptime SLA

Priority: high

Dependencies: Deploy SaaS control plane with high availability (4563269b-329a-416e-a3e1-c9f5633a07b3)

Acceptance Criteria:

• Functional: Control plane services deployed in multiple availability zones
• Technical: Load balancers detect unhealthy instances and route traffic accordingly
• Technical: Rolling updates do not cause downtime
• Technical: Failover tested and documented
• Technical: Uptime monitored and reported

Story Points: 5

Estimated Effort: 8 hours

Epic: Performance and Scalability

This epic addresses the platform's performance and scalability requirements to ensure low latency, high throughput, and support for large numbers of clusters and users.

Optimize data processing pipelines for billing and cost attribution

As the system, I want billing data ingestion and cost attribution pipelines to be efficient and scalable, so that cost data is processed timely for large data volumes

Priority: high

Dependencies: Integrate with AWS, GCP, and Azure billing APIs for data import (8b0deef1-efd1-4feb-872f-d45462a5fc86), Implement cost attribution algorithms mapping billing data to Kubernetes workloads (08cf898b-55fc-40b0-ae4e-e0fa9f6983aa)

Acceptance Criteria:

• Functional: Billing data imports complete within configured batch windows
• Functional: Cost attribution processing scales with data volume
• Technical: Pipelines use batching, parallelism, and caching
• Technical: Failures are retried and logged
• Technical: Pipeline metrics collected and monitored

Story Points: 5

Estimated Effort: 8 hours

Implement horizontal scaling for backend microservices

As the DevOps team, I want backend services to scale horizontally, so that the platform can handle increasing load and cluster counts

Priority: high

Dependencies: Deploy SaaS control plane with high availability (4563269b-329a-416e-a3e1-c9f5633a07b3)

Acceptance Criteria:

• Functional: Services can be deployed with Kubernetes HPA and scale based on CPU and request metrics
• Technical: Services are stateless and support rolling updates
• Technical: Load balancers distribute traffic evenly
• Technical: Scaling events logged and monitored
• Technical: No service downtime during scaling

Story Points: 5

Estimated Effort: 8 hours

Ensure promotion preview API response time under 5 seconds (95th percentile)

As a platform engineer, I want promotion preview API responses to be fast, so that user experience is smooth and efficient

Priority: high

Dependencies: Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4), Integrate cost estimation engine for promotion previews (ae034446-cc87-4de4-96e0-eb8d6e7eb2b1), Implement Promotion Preview API endpoints (04b3bc3a-e141-444a-b4f4-07df9255b107)

Acceptance Criteria:

• Functional: 95% of promotion preview API requests complete within 5 seconds
• Technical: Backend services optimized for query performance and caching
• Technical: Load testing simulates 1000+ clusters and 10,000+ concurrent users
• Technical: Monitoring tracks API latency and throughput
• Technical: Bottlenecks identified and mitigated

Story Points: 5

Estimated Effort: 8 hours

Epic: Security and SOC2 Compliance

This epic covers all security and compliance requirements including scoped RBAC, strict read-only agent boundaries, audit trails, and SOC2 controls to build enterprise trust.

Achieve SOC2 security standards compliance

As the security team, I want the platform to comply with SOC2 Type II security standards, so that enterprise trust and regulatory requirements are met

Priority: high

Dependencies: Implement scoped RBAC with least privilege enforcement (e62fd760-533a-4196-a8c4-1332a8367689), Maintain full immutable audit trails of all user and system actions (17ced790-e14c-4f86-bc6b-15fa5604b76a)

Acceptance Criteria:

• Functional: Platform implements controls for security, availability, processing integrity, confidentiality, and privacy
• Functional: Regular penetration testing and security reviews conducted
• Technical: Automated compliance evidence collection integrated (e.g., Drata/Vanta)
• Technical: Incident response and monitoring processes documented and operational
• Technical: All data encrypted at rest (AES-256) and in transit (TLS 1.3)

Story Points: 8

Estimated Effort: 13 hours

Maintain full immutable audit trails of all user and system actions

As a compliance officer, I want all actions logged immutably with exportable audit trails, so that SOC2 compliance and forensic analysis are supported

Priority: high

Dependencies: Support Git-first workflow for promotion actions (9a8b8cb4-48be-4843-bdb4-a19d3266217e), Implement audit trail of all promotion changes via Git history (d9290039-8c4c-4122-a6c8-502ecaeda7c2)

Acceptance Criteria:

• Functional: All user and system actions recorded in audit_logs table
• Functional: Audit logs are append-only and tamper-evident
• Functional: Audit logs exportable in JSON and CSV formats
• Technical: Audit log service enforces write-once semantics
• Technical: Audit logs indexed for fast querying and retention policies

Story Points: 5

Estimated Effort: 8 hours

Ensure agent is strictly read-only with no write permissions

As the system, I want the Kubernetes agent to operate with strict read-only permissions enforced by RBAC and code, so that no direct cluster mutation occurs

Priority: high

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9)

Acceptance Criteria:

• Functional: Agent cannot create, update, or delete any Kubernetes resources
• Technical: Kubernetes RBAC roles scoped to read-only verbs only
• Technical: Agent code prevents any write API calls
• Technical: Agent behavior verified by security audits and penetration tests
• Technical: Violations trigger alerts and agent shutdown

Story Points: 5

Estimated Effort: 8 hours

Implement scoped RBAC with least privilege enforcement

As a security officer, I want RBAC enforced at user, team, and cluster levels with least privilege, so that access is controlled and compliant

Priority: high

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Implement backend APIs for cluster and agent data (1cadb28e-4850-45d0-98d3-7e1cb151a532)

Acceptance Criteria:

• Functional: Users can only access resources per assigned roles and clusters
• Functional: RBAC roles and bindings manageable via API and UI
• Technical: RBAC enforced at API, database, and agent levels
• Technical: RBAC rules stored in rbac_roles and rbac_role_bindings tables
• Technical: Unauthorized access attempts are logged and alerted

Story Points: 8

Estimated Effort: 13 hours

Epic: Hybrid Delivery Model Infrastructure

This epic covers the infrastructure and deployment model supporting a hybrid delivery with a SaaS control plane and per-cluster agents deployed via Helm, ensuring high availability and secure communication.

Implement secure communication between control plane and agents

As the system, I want to ensure secure, encrypted communication between the SaaS control plane and per-cluster agents, so that data integrity and confidentiality are maintained

Priority: high

Dependencies: Implement secure communication channel between agent and control plane (dac1050d-6dce-4f33-984e-d7ce648fd7be)

Acceptance Criteria:

• Functional: All communication uses TLS 1.3 with mutual authentication
• Technical: Certificates are rotated automatically and securely
• Technical: Communication channels are outbound-only from agents
• Technical: Communication failures trigger retries and alerts
• Technical: Communication logs are monitored for anomalies

Story Points: 5

Estimated Effort: 8 hours

Implement Helm packaging and versioning for per-cluster agent deployment

As the DevOps team, I want to package and version the Kubernetes agent as a Helm chart, so that it can be installed and upgraded reliably across clusters

Priority: high

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9)

Acceptance Criteria:

• Functional: Helm chart supports installation, upgrade, and rollback
• Functional: Chart includes configurable RBAC scopes and TLS certs
• Technical: Chart versioning follows semantic versioning
• Technical: Chart passes linting and integration tests
• Technical: Chart documentation included for users

Story Points: 5

Estimated Effort: 8 hours

Deploy SaaS control plane with high availability

As the DevOps team, I want to deploy the SaaS control plane on cloud infrastructure with high availability, so that the platform is reliable and resilient

Priority: high

Acceptance Criteria:

• Functional: Control plane services deployed in multi-AZ Kubernetes cluster
• Functional: Services are stateless and support rolling updates
• Technical: Load balancers distribute traffic with health checks
• Technical: Failover and auto-scaling configured for critical services
• Technical: Monitoring and alerting configured for uptime SLA

Story Points: 8

Estimated Effort: 13 hours

Epic: Cloud Provider Billing APIs Integration

This epic covers integration with AWS, GCP, and Azure billing APIs to import cost data for FinOps reconciliation and cost impact previews, ensuring secure and regular data synchronization.

Implement regular data synchronization for billing data imports

As the system, I want to schedule and execute regular billing data imports from cloud providers, so that cost data is up-to-date for reconciliation and previews

Priority: high

Dependencies: Integrate with AWS, GCP, and Azure billing APIs for data import (8b0deef1-efd1-4feb-872f-d45462a5fc86), Implement secure API authentication and authorization for cloud billing APIs (02649795-6a85-4141-9e3e-9fdaf1e4dab9)

Acceptance Criteria:

• Functional: Data imports run on configurable schedules (e.g., daily at midnight)
• Functional: Import status and errors are logged and visible in UI
• Technical: Import jobs handle incremental data and avoid duplicates
• Technical: Import failures trigger retries with exponential backoff
• Technical: Import metadata stored in billing_imports table

Story Points: 5

Estimated Effort: 8 hours

Implement secure API authentication and authorization for cloud billing APIs

As the system, I want to securely authenticate and authorize API clients for AWS, GCP, and Azure billing APIs, so that billing data can be imported safely

Priority: high

Dependencies: Integrate with AWS, GCP, and Azure billing APIs for data import (8b0deef1-efd1-4feb-872f-d45462a5fc86)

Acceptance Criteria:

• Functional: API clients use encrypted credentials stored securely
• Functional: Credentials support rotation and revocation
• Technical: Use OAuth2, IAM roles, or service accounts as appropriate per provider
• Technical: Access tokens or keys never logged or exposed
• Technical: Authentication failures trigger alerts and retries

Story Points: 5

Estimated Effort: 8 hours

Epic: Auto-Remediation with Human Approval

This epic covers automated remediation triggered by detected issues, requiring explicit human approval before execution, with audit trails to reduce operational risk while preserving control.

Implement remediation executor to create Git PRs upon approval

As the system, I want to create Git pull requests for remediation actions after human approval, so that remediation changes flow through GitOps pipelines without direct cluster mutation

Priority: medium

Dependencies: Implement UI and API for human approval workflows (aba0e092-32c4-448f-b2a8-85e294e5984a), Support Git-first workflow for promotion actions (9a8b8cb4-48be-4843-bdb4-a19d3266217e)

Acceptance Criteria:

• Functional: Approved remediation actions trigger Git commit and PR creation
• Functional: PR links are displayed in UI and stored in DB
• Technical: GitOps Orchestrator integrates with remediation executor
• Technical: Remediation action status updated to executed or failed
• Technical: Audit logs record all remediation execution events

Story Points: 5

Estimated Effort: 8 hours

Implement UI and API for human approval workflows

As an SRE, I want to review and approve or reject remediation actions before execution, so that I retain control over automated fixes

Priority: medium

Dependencies: Define remediation rules and triggers (25a17bb5-1242-43e4-8c0d-85c4eee71a91), Support Git-first workflow for promotion actions (9a8b8cb4-48be-4843-bdb4-a19d3266217e)

Acceptance Criteria:

• Functional: UI lists pending remediation actions with details and approval buttons
• Functional: API supports approval and rejection PATCH endpoints
• Functional: Approval status updates remediation action status and triggers GitOps PR creation
• Technical: Approval actions logged in remediation_approvals and audit_logs tables
• Technical: UI updates in real-time with action status changes

Story Points: 5

Estimated Effort: 8 hours

Define remediation rules and triggers

As an SRE, I want to define remediation rules with triggers based on cluster state or cost anomalies, so that automated remediation can be proposed

Priority: medium

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Implement cost attribution algorithms mapping billing data to Kubernetes workloads (08cf898b-55fc-40b0-ae4e-e0fa9f6983aa)

Acceptance Criteria:

• Functional: UI and API support creation and management of remediation rules
• Functional: Rules specify trigger conditions and remediation actions
• Technical: Rules stored in remediation_rules table with JSONB trigger/action schemas
• Technical: Rule engine evaluates triggers periodically or on events
• Technical: Rule evaluation latency under 1 second per cluster

Story Points: 5

Estimated Effort: 8 hours

Epic: Deep FinOps Billing Reconciliation

This epic includes features to import and process cloud billing data from AWS, GCP, and Azure, map billing data to Kubernetes workloads, and detect cost anomalies post-deployment to enable cost control.

Detect and alert on cost anomalies post-deployment

As an SRE, I want to be alerted when cost spikes or anomalies occur after workload promotions, so that I can investigate and remediate unexpected cloud spend

Priority: high

Dependencies: Implement cost attribution algorithms mapping billing data to Kubernetes workloads (08cf898b-55fc-40b0-ae4e-e0fa9f6983aa)

Acceptance Criteria:

• Functional: System detects cost spikes exceeding configurable thresholds
• Functional: Alerts are sent via UI notifications, email, or Slack
• Technical: Anomaly detection algorithms analyze billing and attribution data
• Technical: Anomalies stored and queryable via API GET /api/v1/costs/anomalies
• Technical: Alerting integrates with monitoring and logging systems

Story Points: 5

Estimated Effort: 8 hours

Implement cost attribution algorithms mapping billing data to Kubernetes workloads

As the system, I want to map cloud billing records to Kubernetes workloads using cluster metadata and resource usage, so that accurate cost attribution is available

Priority: high

Dependencies: Integrate with AWS, GCP, and Azure billing APIs for data import (8b0deef1-efd1-4feb-872f-d45462a5fc86), Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9)

Acceptance Criteria:

• Functional: Cost attributions link billing records to cluster and workload identifiers
• Functional: Attribution data stored in cost_attributions table
• Technical: Algorithms consider resource labels, namespaces, and cluster metadata
• Technical: Attribution processing completes within batch window (e.g., 1 hour)
• Technical: Attribution results are queryable via API and dashboards

Story Points: 8

Estimated Effort: 13 hours

Integrate with AWS, GCP, and Azure billing APIs for data import

As the system, I want to securely connect to cloud provider billing APIs and import billing data regularly, so that cost data is available for reconciliation and previews

Priority: high

Acceptance Criteria:

• Functional: System supports API authentication and authorization for AWS, GCP, and Azure billing APIs
• Functional: Billing data is imported on a configurable schedule (e.g., daily)
• Technical: API clients handle pagination, rate limits, and errors with retries
• Technical: Imported raw billing data stored securely in billing_imports table
• Technical: Data normalization maps provider-specific fields to internal schema

Story Points: 8

Estimated Effort: 13 hours

Epic: Helm Management UI

This epic covers the UI and backend integration for managing Helm charts and releases within Kubernetes clusters, facilitating workload promotions and rollbacks.

Support promotion workflows involving Helm releases

As a platform engineer, I want to promote Helm releases through GitOps workflows, so that Helm-based workloads can be safely deployed and rolled back

Priority: medium

Dependencies: Support Git-first workflow for promotion actions (9a8b8cb4-48be-4843-bdb4-a19d3266217e), Implement UI to view installed Helm releases per cluster (c6e4309e-329b-4c86-b116-5016e702aace), Implement backend APIs for Helm release and chart management (4dd62f85-1bb4-4835-9315-b141d0babddc)

Acceptance Criteria:

• Functional: Promotion approval creates Git commit and PR with Helm release changes
• Functional: UI shows Helm release promotion status and Git PR links
• Technical: GitOps Orchestrator integrates with Helm Management Service
• Technical: Helm release metadata stored and versioned in DB
• Technical: Promotion workflows support rollback of Helm releases

Story Points: 5

Estimated Effort: 8 hours

Implement backend APIs for Helm release and chart management

As the system, I want to provide APIs to list Helm releases and manage Helm charts, so that the UI can facilitate Helm-based workload promotions

Priority: medium

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9)

Acceptance Criteria:

• Functional: API GET /api/v1/clusters/:cluster_id/helm/releases returns Helm release data
• Functional: API GET /api/v1/helm/charts returns available Helm charts
• Functional: API POST /api/v1/helm/charts supports adding new charts
• Technical: APIs enforce OIDC JWT authentication and RBAC
• Technical: APIs respond within 200ms under normal load

Story Points: 3

Estimated Effort: 5 hours

Implement UI to view installed Helm releases per cluster

As a platform engineer, I want to view installed Helm releases in each cluster, so that I can manage workloads deployed via Helm

Priority: medium

Dependencies: Implement backend APIs for Helm release and chart management (4dd62f85-1bb4-4835-9315-b141d0babddc)

Acceptance Criteria:

• Functional: UI lists Helm releases with release name, chart version, namespace, and status
• Functional: UI supports filtering and sorting Helm releases
• Technical: UI uses React 19 and Material UI
• Technical: Data fetched from Helm Management Service APIs
• Technical: UI loads release list within 2 seconds

Story Points: 3

Estimated Effort: 5 hours

Epic: Cluster Management UI

This epic includes the user interface and backend APIs for managing multiple Kubernetes clusters, monitoring agent installation status, and displaying cluster metadata to simplify cluster operations.

Implement backend APIs for cluster and agent data

As the system, I want to provide backend APIs to fetch cluster metadata and agent installation status, so that the UI can display accurate cluster information

Priority: medium

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Implement agent heartbeat and status reporting (b60282f6-53c3-4606-97c6-a3df7e241214)

Acceptance Criteria:

• Functional: API GET /api/v1/clusters returns list of clusters with metadata
• Functional: API GET /api/v1/clusters/:id returns detailed cluster info
• Functional: API GET /api/v1/clusters/:id/agent-status returns agent heartbeat and status
• Technical: APIs enforce OIDC JWT authentication and RBAC
• Technical: APIs respond within 200ms under normal load

Story Points: 3

Estimated Effort: 5 hours

Implement cluster list and detail views in UI

As a platform engineer, I want to view a list of all registered Kubernetes clusters and see detailed metadata and agent status for each cluster, so that I can manage clusters effectively

Priority: medium

Dependencies: Implement backend APIs for cluster and agent data (1cadb28e-4850-45d0-98d3-7e1cb151a532)

Acceptance Criteria:

• Functional: UI displays paginated list of clusters with name, cloud provider, region, and status
• Functional: Detail view shows agent installation status, last heartbeat, and cluster metadata
• Technical: UI built with React 19 and Material UI
• Technical: Data fetched from Cluster Management Service APIs
• Technical: UI loads cluster list within 2 seconds

Story Points: 3

Estimated Effort: 5 hours

Epic: LLM Explanation Engine for Promotion Risk and Cost

This epic covers the integration and implementation of a large language model explanation engine that generates plain-English reasoning for promotion risks and cost impacts, improving user understanding and decision-making.

Implement prompt engineering and contextual data extraction for LLM

As the system, I want to build prompt templates and extract relevant promotion metadata to provide contextually accurate explanations from the LLM

Priority: medium

Dependencies: Integrate with LLM API for explanation generation (29189df5-ffef-42fc-8e8c-7193990974ab)

Acceptance Criteria:

• Functional: Prompt builder formats promotion diff, cost, and risk data into LLM input
• Functional: Sensitive data such as secrets and cluster names are redacted
• Technical: Prompt templates are versioned and stored in config
• Technical: Context extraction is performant and completes within 1 second
• Technical: Explanations are consistent and accurate based on test cases

Story Points: 3

Estimated Effort: 5 hours

Implement UI integration for displaying LLM explanations

As a platform engineer, I want to see LLM-generated plain-English explanations in the promotion preview UI, so that I can better understand risk and cost factors

Priority: high

Dependencies: Integrate with LLM API for explanation generation (29189df5-ffef-42fc-8e8c-7193990974ab), Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4), Display promotion risk assessment in preview UI (f20c53bf-79c6-43f6-9f0b-9170d20c61af)

Acceptance Criteria:

• Functional: Explanation text is displayed alongside risk and cost previews
• Functional: UI updates explanations in real-time after preview generation
• Technical: UI implemented using React 19 and Material UI components
• Technical: Explanations are sanitized to remove sensitive data
• Technical: Explanations load within 2 seconds in UI

Story Points: 3

Estimated Effort: 5 hours

Integrate with LLM API for explanation generation

As the system, I want to integrate with an LLM API (OpenAI/Azure OpenAI) to generate human-readable explanations for promotion risk and cost, so that users can understand complex implications

Priority: high

Dependencies: Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4), Integrate cost estimation engine for promotion previews (ae034446-cc87-4de4-96e0-eb8d6e7eb2b1), Display promotion risk assessment in preview UI (f20c53bf-79c6-43f6-9f0b-9170d20c61af)

Acceptance Criteria:

• Functional: System sends sanitized promotion metadata to LLM API
• Functional: System receives and stores explanation text linked to promotion
• Technical: API keys stored securely and usage monitored
• Technical: Rate limiting and error handling implemented with retries
• Technical: Explanations cached in Redis to reduce API calls

Story Points: 5

Estimated Effort: 8 hours

Epic: GitOps Orchestration for Promotion Actions

This epic includes all features related to orchestrating workload promotion actions through Git repositories, ensuring auditability, traceability, and conflict management.

Implement conflict detection and resolution support in GitOps workflows

As the system, I want to detect conflicts in Git promotion PRs and provide resolution mechanisms, so that promotion workflows are reliable and consistent

Priority: medium

Dependencies: Integrate with Git providers for promotion workflows (71cb6fa4-b794-4015-8fba-b5f6ba179de4), Implement automatic commit and pull request creation for promotions (a29e7201-a44f-432d-8098-4ba5dbef1d88)

Acceptance Criteria:

• Functional: System detects merge conflicts before PR creation
• Functional: UI alerts users of conflicts with actionable messages
• Technical: Conflict detection uses Git merge checks and diff analysis
• Technical: System supports manual conflict resolution via UI or CLI
• Technical: Conflicts logged in audit trail

Story Points: 5

Estimated Effort: 8 hours

Implement audit trail of all promotion changes via Git history

As a security/compliance officer, I want all promotion changes to be auditable via Git commit history, so that compliance and traceability are ensured

Priority: high

Dependencies: Integrate with Git providers for promotion workflows (71cb6fa4-b794-4015-8fba-b5f6ba179de4), Implement automatic commit and pull request creation for promotions (a29e7201-a44f-432d-8098-4ba5dbef1d88)

Acceptance Criteria:

• Functional: All promotion-related Git commits include user and timestamp metadata
• Functional: Audit logs link Git commits to promotion actions
• Technical: AuditLogService records immutable entries for promotion Git events
• Technical: Audit logs are exportable and tamper-evident
• Technical: Audit logs stored in PostgreSQL with indexing for fast queries

Story Points: 3

Estimated Effort: 5 hours

Implement automatic commit and pull request creation for promotions

As the system, I want to automatically create Git commits and pull requests when a promotion is approved, so that changes flow through GitOps pipelines

Priority: high

Dependencies: Integrate with Git providers for promotion workflows (71cb6fa4-b794-4015-8fba-b5f6ba179de4), Support Git-first workflow for promotion actions (9a8b8cb4-48be-4843-bdb4-a19d3266217e)

Acceptance Criteria:

• Functional: Approving a promotion triggers Git commit with workload changes
• Functional: Pull request is created with appropriate title and description
• Functional: PR links back to promotion record in UI
• Technical: Conflict detection prevents duplicate or conflicting PRs
• Technical: PR status updates reflected in promotion status

Story Points: 5

Estimated Effort: 8 hours

Integrate with Git providers for promotion workflows

As the system, I want to integrate with GitHub, GitLab, and Bitbucket APIs to manage promotion workflows, so that all changes are auditable via Git

Priority: high

Acceptance Criteria:

• Functional: System can authenticate with Git providers using OAuth2 or PAT
• Functional: System can create commits and pull requests in target repos
• Technical: Webhooks or polling mechanisms detect PR status changes
• Technical: Git metadata stored in git_repos, git_commits, and git_pull_requests tables
• Technical: API rate limits and errors handled gracefully with retries

Story Points: 5

Estimated Effort: 8 hours

Epic: Lightweight Read-Only Kubernetes Agent

This epic covers the development and deployment of a lightweight, read-only Kubernetes agent installed via Helm that securely collects cluster state without mutating resources, ensuring security and trust.

Create Helm chart documentation and installation guides for agent

As a platform engineer, I want clear documentation and guides for installing and upgrading the Kubernetes agent via Helm, so that I can deploy the agent reliably

Priority: medium

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9)

Acceptance Criteria:

• Functional: Documentation includes prerequisites, installation commands, and upgrade procedures
• Functional: Troubleshooting section covers common errors and fixes
• Technical: Helm chart supports configurable RBAC scopes and TLS certs
• Technical: Documentation is accessible via UI and public docs site
• Technical: Installation process tested on AWS EKS, GCP GKE, and Azure AKS

Story Points: 3

Estimated Effort: 5 hours

Implement agent heartbeat and status reporting

As the system, I want the agent to send periodic heartbeat signals and status updates to the control plane, so that agent health and connectivity can be monitored

Priority: medium

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9), Implement secure communication channel between agent and control plane (dac1050d-6dce-4f33-984e-d7ce648fd7be)

Acceptance Criteria:

• Functional: Agent sends heartbeat every configurable interval (default 60s)
• Functional: Control plane UI displays agent online/offline status
• Technical: Heartbeat messages include agent version, last sync time, and error states
• Technical: Heartbeat failures trigger alerts in monitoring system
• Technical: Heartbeat data stored in cluster_agents table

Story Points: 3

Estimated Effort: 5 hours

Implement secure communication channel between agent and control plane

As the system, I want the agent to communicate securely with the SaaS control plane using encrypted TLS channels and mutual authentication, so that data integrity and confidentiality are ensured

Priority: high

Dependencies: Develop Kubernetes read-only agent with Helm packaging (586b67cb-d8b8-4d0e-a263-38a29777d0c9)

Acceptance Criteria:

• Functional: Agent establishes outbound-only TLS 1.3 connection to control plane
• Technical: Mutual TLS authentication is implemented with certificate rotation
• Technical: All data transmitted is signed and encrypted
• Technical: Communication latency is minimal (<200ms round trip)
• Technical: Connection failures trigger exponential backoff and retries

Story Points: 5

Estimated Effort: 8 hours

Develop Kubernetes read-only agent with Helm packaging

As the system, I want to develop a lightweight Kubernetes agent that operates in read-only mode and can be installed via Helm, so that cluster state can be collected securely

Priority: high

Acceptance Criteria:

• Functional: Agent collects cluster resource metadata without mutating any resources
• Functional: Agent can be installed and upgraded via Helm 3.15 chart
• Technical: Agent uses Kubernetes RBAC scoped for read-only access
• Technical: Agent has low CPU and memory footprint (<50MiB RAM, <100m CPU)
• Technical: Agent communicates securely with SaaS control plane using TLS 1.3

Story Points: 8

Estimated Effort: 13 hours

Epic: Kubernetes Workload Promotion Preview

This epic covers all features related to previewing Kubernetes workload promotions, including diffs, cost impact, GitOps integration, and risk assessment. It enables platform teams to make informed decisions before promoting workloads to production, reducing risk and unexpected costs.

Implement Promotion Preview API endpoints

As the system, I want to expose REST and gRPC endpoints for promotion preview creation, retrieval, approval, and rejection, so that clients can interact with promotion workflows

Priority: high

Dependencies: Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4), Integrate cost estimation engine for promotion previews (ae034446-cc87-4de4-96e0-eb8d6e7eb2b1), Support Git-first workflow for promotion actions (9a8b8cb4-48be-4843-bdb4-a19d3266217e), ()

Acceptance Criteria:

• Functional: POST /api/v1/promotions/preview accepts workload promotion preview requests
• Functional: GET /api/v1/promotions/:id returns promotion details including diff, cost, risk, and explanation
• Functional: PATCH endpoints for approval and rejection update promotion status and trigger GitOps actions
• Technical: Endpoints enforce OIDC JWT authentication and RBAC authorization
• Technical: API responses conform to documented JSON schema and error handling

Story Points: 5

Estimated Effort: 8 hours

Display promotion risk assessment in preview UI

As a platform engineer, I want to see a risk assessment for workload promotions, so that I can evaluate potential operational risks before approval

Priority: high

Dependencies: Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4)

Acceptance Criteria:

• Functional: UI shows risk level (low, medium, high, critical) and risk factors for each promotion
• Functional: Risk details include resource quota breaches, environment drift, and policy violations
• Technical: RiskAnalyzer service computes risk based on cluster state and promotion diff
• Technical: Risk data stored in promotion_risks table linked to promotion
• Technical: Risk assessment API responds within 5 seconds

Story Points: 5

Estimated Effort: 8 hours

Support Git-first workflow for promotion actions

As a platform engineer, I want all promotion actions to flow through Git with automatic commit and pull request creation, so that changes are auditable and traceable

Priority: high

Dependencies: Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4), Display promotion risk assessment in preview UI (f20c53bf-79c6-43f6-9f0b-9170d20c61af)

Acceptance Criteria:

• Functional: Promotion approval triggers automatic Git commit and PR creation
• Functional: UI displays Git PR URL and status for each promotion
• Technical: GitOps Orchestrator integrates with GitHub/GitLab/Bitbucket APIs
• Technical: Conflict detection and resolution supported during PR creation
• Technical: Promotion Git commits and PRs linked to workload_promotions in DB

Story Points: 5

Estimated Effort: 8 hours

Integrate cost estimation engine for promotion previews

As a platform engineer, I want to see the cost impact of workload promotions, so that I can anticipate cloud spend changes before deployment

Priority: high

Dependencies: Preview workload diffs between staging and production (571c15f7-5c32-40e5-ac1f-54a7841f54c4), Integrate with AWS, GCP, and Azure billing APIs for data import (8b0deef1-efd1-4feb-872f-d45462a5fc86)

Acceptance Criteria:

• Functional: Cost preview shows baseline, projected, and delta monthly costs in USD
• Functional: UI displays resource usage deltas (CPU, memory) alongside cost impact
• Technical: Cost engine aggregates cluster state and billing data to estimate costs
• Technical: Cost preview API responds within 5 seconds (95th percentile)
• Technical: Cost preview data stored in promotion_costs table linked to promotion

Story Points: 5

Estimated Effort: 8 hours

Preview workload diffs between staging and production

As a platform engineer, I want to preview the diffs of Kubernetes workload changes between staging and production, so that I can understand what will change before promotion

Priority: high

Dependencies: Integrate cost estimation engine for promotion previews (ae034446-cc87-4de4-96e0-eb8d6e7eb2b1), Support Git-first workflow for promotion actions (9a8b8cb4-48be-4843-bdb4-a19d3266217e)

Acceptance Criteria:

• Functional: User can request a diff preview for a workload promotion specifying source and target namespaces
• Functional: UI displays added, removed, and modified Kubernetes resources in a clear diff format
• Technical: System fetches read-only cluster state via Kubernetes API and agent snapshots
• Technical: Diff computation completes within 5 seconds for typical workloads
• Technical: Diff data is stored in PostgreSQL workload_diffs table linked to promotion

Story Points: 5

Estimated Effort: 8 hours

Spike Stories (standalone)

Spike: Documentation framework setup

Spike

As a technical writer, I want to set up documentation frameworks for API docs and user guides, so that documentation is maintainable and accessible

Priority: medium

Timebox: 2 days

Expected Outcomes:

• API documentation generated from OpenAPI specs
• User guides and onboarding docs hosted in docs site
• Documentation CI integration for validation

Acceptance Criteria:

• Docs site accessible and searchable
• API docs updated automatically from code
• Documentation reviewed and approved

Story Points: 2

Spike: Code quality tools setup (ESLint, Prettier, GoLint)

Spike

As a developer, I want to configure code quality and formatting tools, so that codebase is consistent and maintainable

Priority: medium

Timebox: 2 days

Expected Outcomes:

• ESLint and Prettier configured for frontend
• GoLint and formatting configured for backend
• Pre-commit hooks enforce code quality

Acceptance Criteria:

• Code style violations detected in CI
• Developers can run format and lint locally
• Documentation for code standards included

Story Points: 2

Spike: Testing framework setup for frontend and backend

Spike

As a QA engineer, I need to set up unit and integration testing frameworks for frontend and backend, so that code quality is ensured

Priority: high

Timebox: 3 days

Expected Outcomes:

• Frontend testing with Jest and React Testing Library
• Backend testing with Go testing package and mocks
• Integration test scaffolding for API endpoints

Acceptance Criteria:

• Tests run in CI with coverage reports
• Sample tests for critical components included
• Test failures cause CI to fail

Story Points: 3

Spike: Database migration framework and initial schema setup

Spike

As a backend engineer, I need to set up database migration tooling and create initial schema migrations, so that database changes are versioned and reproducible

Priority: high

Timebox: 2 days

Expected Outcomes:

• Migration framework integrated (e.g., golang-migrate)
• Initial schema migration scripts for core tables
• Migration run as part of CI/CD

Acceptance Criteria:

• Migrations can be applied and rolled back reliably
• Schema matches architecture design
• Migration failures cause CI to fail

Story Points: 2

Spike: CI/CD pipeline setup with GitHub Actions

Spike

As a DevOps engineer, I need to set up CI/CD pipelines for build, test, and deployment using GitHub Actions, so that code changes are validated and deployed automatically

Priority: high

Timebox: 3 days

Expected Outcomes:

• CI pipeline for linting, unit tests, and build
• CD pipeline for deploying to test environment
• Integration with GitHub PRs and branch protections

Acceptance Criteria:

• CI runs on every PR and commit
• CD deploys successful builds to staging
• Pipeline failures notify developers

Story Points: 3

Spike: Development environment setup with Docker and Helm

Spike

As a developer, I need to configure local development environment with Docker Compose and Helm charts, so that I can develop and test the agent and control plane locally

Priority: high

Timebox: 3 days

Expected Outcomes:

• Docker Compose files for backend services and database
• Helm chart for local agent deployment
• Scripts for environment setup and teardown

Acceptance Criteria:

• Developers can run full stack locally with one command
• Agent can be installed in local Kubernetes cluster via Helm
• Documentation for environment setup included

Story Points: 3

Spike: Project repository initialization and scaffolding

Spike

As a developer, I need to set up the initial project repository with scaffolding for frontend and backend, so that development can start with a solid foundation

Priority: high

Timebox: 3 days

Expected Outcomes:

• Initialized monorepo with React 19 frontend and Go 1.22 backend
• Basic folder structure and build scripts
• README with setup instructions

Acceptance Criteria:

• Repository created with frontend and backend folders
• Build scripts for local development and production builds
• Documentation for initial setup and running

Story Points: 3

Engineering Tasks

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

Initialize project repository with frontend and backend scaffolding

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Create a monorepo structure with separate folders for frontend (React 19 + TypeScript) and backend (Go 1.22 microservices). Include base configuration files such as tsconfig.json, go.mod, and package.json. Set up initial folder structure following architecture guidelines.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 3h (2x factor)
• AI tools: Devin (Cognition), GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Repository contains frontend and backend folders with base scaffolding
• Build scripts for local development and production builds are included
• Project builds successfully without errors

Definition of Done:

• Code is committed to repository
• Build scripts run successfully
• Code review completed

Create initial database schema migration scripts

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 5a8a9302-c074-46b6-8d6e-222c43aa7cd8
• Critical path: No
• Description: Develop initial migration scripts to create core tables as per architecture design, including clusters, agents, promotions, audit logs, and billing data tables with appropriate columns and constraints.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3h (1.7x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Initial schema matches architecture specifications
• Migrations can be applied and rolled back without errors
• Schema includes primary keys, foreign keys, and indexes

Definition of Done:

• Migration scripts committed and tested
• Schema validated against architecture
• Code review completed

Write sample unit and integration tests for critical components

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): bcb0d113-96a0-45ee-bbaa-09ce63365a67
• Critical path: No
• Description: Develop representative unit tests for frontend components and backend services, plus integration tests covering key workflows to demonstrate test framework usage and coverage.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 4h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Sample tests cover at least 3 critical frontend components
• Sample tests cover at least 3 backend service functions
• Tests achieve minimum 80% coverage

Definition of Done:

• Tests committed and passing
• Coverage reports generated
• Code review completed

Configure Docker Compose for local full stack environment

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Create Docker Compose files to run backend microservices, frontend, and dependencies (e.g., PostgreSQL, Redis) locally. Ensure environment variables and networking are configured for seamless local development.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 3h (2x factor)
• AI tools: Devin (Cognition)
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Developers can start full stack locally with a single docker-compose up command
• All services communicate correctly in local environment
• Docker Compose supports environment variable overrides

Definition of Done:

• Docker Compose files committed and tested
• Local environment starts without errors
• Documentation updated with usage instructions

Create Helm chart for local Kubernetes agent deployment

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop a Helm chart to deploy the Kubernetes read-only agent in local Kubernetes clusters (e.g., kind, minikube). Include values.yaml with minimal configuration options for quick setup.
• Estimate: 5 hours
• Story Points: 2
• Traditional estimate: 5h | AI-accelerated: 3h (1.7x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Agent can be installed in local Kubernetes cluster using helm install
• Chart supports configuration overrides via values.yaml
• Agent runs in read-only mode with correct RBAC

Definition of Done:

• Helm chart committed and tested in local cluster
• Installation documented with example commands
• Code review completed

Implement CD pipeline to deploy to staging environment

• Type: devops | Priority: high | Status: todo
• Depends on (complete these first): 241d8038-107b-426a-bb1c-e077ee900496
• Critical path: No
• Description: Extend GitHub Actions workflows to deploy successful builds to a staging environment automatically. Include rollback steps and notifications on failure.
• Estimate: 5 hours
• Story Points: 2
• Traditional estimate: 5h | AI-accelerated: 3h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• CD pipeline deploys to staging on successful build
• Rollback is possible on deployment failure
• Developers receive notifications on failures

Definition of Done:

• CD workflows committed and tested
• Deployments succeed and rollback tested
• Notification channels configured

Set up documentation framework with static site generator

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Configure a documentation site using a static site generator (e.g., Docusaurus or MkDocs) to host API docs and user guides. Integrate with repository for automatic deployment.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2h (2x factor)
• AI tools: Devin (Cognition)
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Documentation site builds successfully
• Site is accessible locally and via deployment
• Supports markdown docs and search functionality

Definition of Done:

• Documentation framework committed and tested
• Site deployed to staging environment
• Documentation team reviews setup

Write initial project setup and running documentation

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): cc95f296-c7bc-4b59-b3f6-2d9e3ad83dc4, 215f40be-c234-4e3c-a2e8-94762d5b6d29
• Critical path: No
• Description: Create README and developer guide documentation describing repository structure, how to build and run frontend and backend locally, and how to run production builds.
• 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 includes repository structure overview
• Build and run instructions for frontend and backend are clear
• Documentation is reviewed and approved by team

Definition of Done:

• Documentation is committed and accessible in repo
• Reviewed by at least one team member
• No spelling or formatting errors

Document code quality standards and usage instructions

• Type: documentation | Priority: medium | Status: todo
• Depends on (complete these first): 7adad9b6-a5a7-4b85-affa-7a82467acf58, 3bbafd52-7f23-4033-9fc6-1dd63efd585e
• Critical path: No
• Description: Write documentation describing code style rules, linting and formatting commands, and how to fix violations for both frontend and backend developers.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (2x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers ESLint, Prettier, GoLint usage
• Includes instructions for running locally and in CI
• Reviewed and approved by team

Definition of Done:

• Documentation committed and accessible
• Reviewed by developers
• No spelling or formatting errors

Implement connection failure handling with exponential backoff and retries

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop logic in the agent to detect connection failures to the control plane and retry connections using exponential backoff. Ensure minimal communication latency (<200ms round trip) under normal conditions.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.7h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Agent retries connection with exponential backoff on failure
• Communication latency remains under 200ms round trip
• Retries do not cause resource exhaustion

Definition of Done:

• Code implemented and reviewed
• Unit tests simulate failures and retries
• Performance tests confirm latency requirements

Configure ESLint and Prettier for frontend code quality

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Set up ESLint with recommended rules and Prettier for code formatting in the frontend React project. Integrate with IDEs and pre-commit hooks for consistent code style.
• 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:

• ESLint and Prettier configs are present and enforce rules
• Pre-commit hooks run lint and format checks
• Developers can run lint and format commands locally

Definition of Done:

• Configurations committed and tested
• Pre-commit hooks working
• Documentation updated

Set up database migration tooling with version control

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Integrate a database migration framework (e.g., Flyway, Goose) into backend services. Configure migration scripts directory and version control to manage schema changes reliably.
• 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:

• Migration tooling is integrated and can apply/rollback migrations
• Migration scripts are versioned and stored in repository
• Migration failures cause CI pipeline to fail

Definition of Done:

• Migration tooling configured and tested locally
• Sample migration scripts committed
• CI pipeline updated to run migrations

Design and implement CI pipeline for build and test

• Type: devops | Priority: high | Status: todo
• Critical path: No
• Description: Create GitHub Actions workflows that trigger on pull requests and commits to main branches. The pipeline should build frontend and backend, run unit and integration tests, and report status.
• 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:

• CI pipeline triggers on PRs and commits
• Builds for frontend and backend succeed
• Tests run and results reported in PR

Definition of Done:

• CI workflows committed and tested
• Build and test steps pass reliably
• Documentation updated with pipeline usage

Integrate automatic API documentation generation from code

• Type: development | Priority: medium | Status: todo
• Depends on (complete these first): ed585951-1dd2-481d-89f1-9f3a93465713
• Critical path: No
• Description: Configure tools to generate API documentation automatically from backend code annotations (e.g., OpenAPI 4 with Go annotations). Ensure docs update on code changes.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3h (1.7x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• API docs generated automatically from code
• Docs update on code changes and CI runs
• Documentation site includes API docs section

Definition of Done:

• API doc generation scripts committed
• Docs build and deploy successfully
• Reviewed by backend and docs teams

Set up GoLint and formatting tools for backend code

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Configure GoLint and gofmt for backend Go code quality and formatting. Integrate with CI pipeline and provide developer instructions.
• 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:

• GoLint and gofmt checks run locally and in CI
• Code style violations are reported
• Developers can run lint and format commands

Definition of Done:

• Linting tools configured and tested
• CI pipeline updated
• Documentation updated

Write guidelines and review process for documentation

• Type: documentation | Priority: medium | Status: todo
• Depends on (complete these first): ed585951-1dd2-481d-89f1-9f3a93465713, 8d99d693-1386-4a80-9e60-739040418388
• Critical path: No
• Description: Develop documentation standards, contribution guidelines, and review process to ensure maintainability and accessibility of API docs and user guides.
• 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:

• Guidelines cover doc style, structure, and review
• Process includes peer review and approvals
• Documentation team approves guidelines

Definition of Done:

• Guidelines committed and accessible
• Reviewed by documentation team
• No spelling or formatting errors

Store heartbeat data in cluster_agents database table

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Extend the backend database schema to include columns for agent heartbeat data such as last heartbeat timestamp, agent version, last sync time, and error states. Implement data persistence and retrieval APIs.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.7h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• cluster_agents table includes columns for heartbeat data
• Heartbeat data is persisted on receipt from agent
• APIs can retrieve heartbeat and status data

Definition of Done:

• Migration scripts created and tested
• Backend APIs implemented and tested
• Integration tests verify data persistence

Set up Jest and Vitest testing frameworks for frontend and backend

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Configure Jest for React frontend unit tests and Vitest for backend Go microservices unit and integration tests. Integrate with CI pipeline for automated test runs.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3h (1.7x factor)
• AI tools: GitHub Copilot
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Jest and Vitest are configured and runnable locally
• Tests run automatically in CI pipeline
• Coverage reports are generated

Definition of Done:

• Test frameworks configured and tested
• CI pipeline updated
• Documentation updated

Implement CI integration for database migration validation

• Type: devops | Priority: high | Status: todo
• Depends on (complete these first): 5a8a9302-c074-46b6-8d6e-222c43aa7cd8, 68478ed0-676e-41d9-bce1-cb7c9953b27d
• Critical path: No
• Description: Configure CI pipeline to run database migrations on a test database and fail the build if migrations cannot be applied or rolled back cleanly, ensuring migration reliability.
• 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:

• CI pipeline runs migration apply and rollback tests
• Pipeline fails on migration errors
• Test database is cleaned between runs

Definition of Done:

• CI pipeline updated and tested
• Migration failures cause CI failure
• Documentation updated with migration testing steps

Implement outbound TLS 1.3 connection with mutual authentication in agent

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop the agent's secure communication module to establish outbound-only TLS 1.3 connections to the SaaS control plane. Implement mutual TLS authentication using client and server certificates with automated certificate rotation.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.5h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Agent establishes outbound TLS 1.3 connection to control plane
• Mutual TLS authentication is enforced with valid certificates
• Certificate rotation occurs automatically without downtime

Definition of Done:

• Code implemented and reviewed
• Integration tests validate TLS connection and auth
• Certificate rotation tested in staging environment

Document testing framework usage and guidelines

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): bcb0d113-96a0-45ee-bbaa-09ce63365a67, b0c9b5f3-2277-4c4d-add8-9173aa25e646
• Critical path: No
• Description: Write documentation describing how to run tests locally and in CI, interpret coverage reports, and write new tests following project conventions.
• 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 running tests locally and in CI
• Includes instructions for coverage report generation
• Reviewed and approved by QA team

Definition of Done:

• Documentation committed and accessible
• Reviewed by QA and developers
• No spelling or formatting errors

Implement build scripts for local development and production

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): cc95f296-c7bc-4b59-b3f6-2d9e3ad83dc4
• Critical path: No
• Description: Develop npm scripts for frontend and Makefile or shell scripts for backend to support local development builds, hot reload, and production optimized builds. Ensure scripts are documented and easy to use.
• 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:

• Build scripts support local development with hot reload
• Production build scripts generate optimized artifacts
• Scripts are tested and documented

Definition of Done:

• Build scripts run without errors
• Documentation updated with usage instructions
• Code review completed

Document local development environment setup with Docker and Helm

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): 23fb5a53-4b2e-49fc-ba2d-97ab228a204c, dd09e2b6-12d4-435c-b7fd-fbae2d801fe2
• Critical path: No
• Description: Write detailed documentation for developers to set up local environment using Docker Compose and Helm chart for agent installation. Include troubleshooting tips and common commands.
• 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 Docker Compose usage and Helm agent installation
• Includes troubleshooting and environment variable explanations
• Reviewed and approved by team

Definition of Done:

• Documentation committed and accessible
• Reviewed by at least one developer
• No spelling or formatting errors

Document secure communication setup and troubleshooting

• Type: documentation | Priority: high | Status: todo
• Critical path: No
• Description: Write detailed documentation describing the secure communication channel between agent and control plane, including TLS 1.3, mutual authentication, certificate rotation, and retry behavior. Include troubleshooting tips for common connectivity issues.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.6h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers TLS and mutual authentication setup
• Includes certificate rotation procedures
• Troubleshooting section addresses common failures

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Published on public docs site

Design and implement backend API GET /api/v1/clusters

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Develop REST API endpoint GET /api/v1/clusters that returns a paginated list of Kubernetes clusters with metadata including name, cloud provider, region, and status. Enforce OIDC JWT authentication and RBAC authorization.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.4h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• GET /api/v1/clusters returns paginated list with cluster metadata
• API enforces OIDC JWT authentication and RBAC
• API response time under 200ms under normal load

Definition of Done:

• API implemented and reviewed
• Unit and integration tests cover endpoint
• API documented with OpenAPI 4 specs

Configure pipeline failure notifications

• Type: devops | Priority: high | Status: todo
• Depends on (complete these first): 241d8038-107b-426a-bb1c-e077ee900496, 0a847dba-c02c-48ee-aa92-148f58f68535
• Critical path: No
• Description: Set up notifications via email or Slack for pipeline failures to alert developers promptly. Integrate notification steps into CI/CD workflows.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1h (2x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Notifications sent on CI or CD failures
• Notification recipients configured
• Notification content includes failure details

Definition of Done:

• Notifications tested and verified
• Documentation updated
• Code review completed

Create Helm chart for Kubernetes agent with configurable RBAC and TLS

• Type: development | Priority: high | Status: todo
• Depends on (complete these first): 2aa37a9c-2b47-4eea-9c26-8e7a05f88cc2
• Critical path: No
• Description: Develop a Helm 3.15 chart to package the Kubernetes read-only agent. The chart must support configuration of RBAC scopes, TLS certificates, and resource limits. Include values.yaml with sensible defaults and upgrade support.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.5h (1.6x factor)
• AI tools: Cursor
• AI category: boilerplate (high confidence)

Acceptance Criteria:

• Helm chart installs agent with read-only RBAC roles
• TLS certificates can be configured via Helm values
• Agent resource limits configurable and enforced

Definition of Done:

• Helm chart passes linting and install tests
• Chart upgrade tested without downtime
• Documentation updated with Helm usage

Document cloud billing API integration and data import process

• Type: documentation | Priority: high | Status: todo
• Critical path: No
• Description: Write detailed documentation describing how the system connects to AWS, GCP, and Azure billing APIs, authentication methods, data import scheduling, and error handling. Include configuration examples and troubleshooting tips.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.2h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers API authentication and data import scheduling
• Includes configuration examples for each cloud provider
• Troubleshooting section addresses common errors

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Published on public docs site

Document Kubernetes agent installation, configuration, and upgrade process

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): e62c4709-6167-4755-b02a-ccf63d551d36
• Critical path: No
• Description: Create detailed documentation for platform engineers describing prerequisites, Helm installation commands, configuration options for RBAC and TLS, upgrade procedures, and troubleshooting common issues. Publish docs on public site and integrate into UI help.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.2h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation includes installation and upgrade commands
• Troubleshooting section covers common errors and fixes
• Docs accessible via public docs site and UI

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Documentation published and linked in UI

Develop Kubernetes read-only agent core functionality

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement the Kubernetes agent in Go 1.22 that collects cluster resource metadata using Kubernetes API v1.36.1 with read-only permissions enforced by RBAC. Ensure the agent does not mutate any cluster resources and maintains a low CPU (<100m) and memory footprint (<50MiB).
• Estimate: 6 hours
• Story Points: 5
• Traditional estimate: 6h | AI-accelerated: 4h (1.5x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Agent collects cluster resource metadata without mutating any resources
• Agent runs with CPU usage under 100m and memory under 50MiB
• RBAC permissions scoped strictly for read-only access

Definition of Done:

• Code implemented and reviewed
• Unit and integration tests cover 80% of agent code
• Performance benchmarks confirm resource usage limits

Write unit and integration tests for secure communication module

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Develop unit tests for TLS connection establishment, mutual authentication, certificate rotation, data signing, and retry logic. Write integration tests simulating control plane connectivity and failure scenarios.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover all secure communication components with 80% coverage
• Integration tests simulate TLS handshake and failure recovery
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated and reviewed
• Test failures block CI pipeline

Develop React component for cluster list view

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Implement a React 19 component using Material UI to display a paginated list of registered Kubernetes clusters. Show cluster name, cloud provider, region, and status. Fetch data from backend API with JWT authentication.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Bolt.new
• AI category: implementation (high confidence)

Acceptance Criteria:

• UI displays paginated cluster list with required fields
• Data fetched securely from backend API
• UI loads cluster list within 2 seconds

Definition of Done:

• Component implemented and reviewed
• Unit tests cover UI logic
• Performance tested for load time

Implement secure data signing and encryption for agent communication

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Ensure all data transmitted between the agent and control plane is signed and encrypted to maintain integrity and confidentiality. Integrate cryptographic signing of payloads and verify signatures on receipt.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.4h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• All transmitted data is cryptographically signed
• Data encryption is enforced end-to-end
• Control plane verifies signatures and rejects invalid data

Definition of Done:

• Code implemented and reviewed
• Unit tests cover signing and verification
• Integration tests validate end-to-end encryption

Write unit and integration tests for agent heartbeat and status reporting

• Type: testing | Priority: medium | Status: todo
• Critical path: No
• Description: Develop unit tests for agent heartbeat message generation and transmission logic. Write integration tests for backend API handling of heartbeat data and frontend UI display of agent status. Ensure 80% coverage and CI integration.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover heartbeat generation and backend processing
• Integration tests verify UI status display
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated and reviewed
• Test failures block CI pipeline

Implement agent heartbeat message generation and transmission

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Develop functionality in the agent to send periodic heartbeat messages every configurable interval (default 60 seconds) to the control plane. Heartbeat messages must include agent version, last sync time, and error states.
• 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:

• Agent sends heartbeat messages at configurable intervals (default 60s)
• Heartbeat includes agent version, last sync time, and error states
• Heartbeat messages are received and processed by control plane

Definition of Done:

• Code implemented and reviewed
• Unit tests cover heartbeat generation and sending
• Integration tests verify control plane reception

Implement UI display of agent online/offline status

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Develop frontend React 19 component to display agent heartbeat status, including online/offline indicator, last heartbeat timestamp, agent version, and error states. Fetch data from backend APIs and update UI in real-time or on refresh.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: Bolt.new
• AI category: implementation (high confidence)

Acceptance Criteria:

• UI shows agent online/offline status accurately
• Displays last heartbeat time and agent version
• UI updates status on refresh or real-time events

Definition of Done:

• Component implemented and reviewed
• Unit tests cover UI logic
• E2E tests verify status display

Document agent heartbeat feature and troubleshooting

• Type: documentation | Priority: medium | Status: todo
• Critical path: No
• Description: Write documentation describing the agent heartbeat mechanism, configuration options for interval, data included in heartbeat messages, and how to interpret agent status in the UI. Include troubleshooting tips for heartbeat failures.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.6h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers heartbeat configuration and data fields
• Includes troubleshooting section for common issues
• Documentation published and accessible in UI and docs site

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Published on public docs site

Document backend cluster and agent APIs

• Type: documentation | Priority: medium | Status: todo
• Critical path: No
• Description: Write API documentation for cluster list, detail, and agent status endpoints including request parameters, response schemas, authentication requirements, and example requests/responses. Integrate docs with OpenAPI 4 and publish on docs site.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.6h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• API docs include request/response schemas and examples
• Authentication and RBAC requirements documented
• Documentation published and accessible on docs site

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Published on public docs site

Design and implement backend API GET /api/v1/clusters/:id and /agent-status

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Develop REST API endpoints GET /api/v1/clusters/:id to return detailed cluster metadata and GET /api/v1/clusters/:id/agent-status to return agent heartbeat and status information. Enforce authentication and RBAC.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.4h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• GET /api/v1/clusters/:id returns detailed cluster info
• GET /api/v1/clusters/:id/agent-status returns heartbeat and status
• APIs enforce OIDC JWT authentication and RBAC

Definition of Done:

• Endpoints implemented and reviewed
• Unit and integration tests cover functionality
• API documentation updated

Write unit and integration tests for cluster and agent data APIs

• Type: testing | Priority: medium | Status: todo
• Critical path: No
• Description: Develop unit tests for the cluster list and detail API handlers and integration tests to verify authentication, RBAC, and data correctness. Ensure tests cover error cases and achieve 80% coverage.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.7h (1.4x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover API handlers with 80% coverage
• Integration tests verify authentication and RBAC enforcement
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated
• Test failures block CI pipeline

Develop React component for cluster detail and agent status view

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Implement a React 19 component to display detailed metadata for a selected cluster including agent installation status, last heartbeat, and cluster metadata. Integrate with backend APIs and Material UI.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: Bolt.new
• AI category: implementation (high confidence)

Acceptance Criteria:

• Detail view shows agent installation status and last heartbeat
• Displays cluster metadata accurately
• Data fetched securely from backend APIs

Definition of Done:

• Component implemented and reviewed
• Unit tests cover UI logic
• Integration tests verify data fetching

Write unit and integration tests for cluster management UI components

• Type: testing | Priority: medium | Status: todo
• Critical path: No
• Description: Develop unit tests for ClusterList and ClusterDetail React components using Jest and React Testing Library. Write integration tests to verify API data fetching and UI rendering. Ensure 80% coverage and CI integration.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.7h (1.4x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover UI components with 80% coverage
• Integration tests verify API calls and data rendering
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated
• Test failures block CI pipeline

Document cost attribution algorithms and API usage

• Type: documentation | Priority: high | Status: todo
• Critical path: No
• Description: Write technical documentation describing the cost attribution methodology, data inputs, and outputs. Document API usage for querying attribution results with examples and configuration options.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.6h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation explains attribution algorithms clearly
• Includes API request and response examples
• Published and accessible on docs site

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Published on public docs site

Document cluster management UI features and usage

• Type: documentation | Priority: medium | Status: todo
• Critical path: No
• Description: Write user documentation describing the cluster list and detail views, including how to interpret cluster metadata and agent status. Include screenshots and troubleshooting tips. Publish in user guides and UI help sections.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.6h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers UI features and data interpretation
• Includes screenshots and usage examples
• Published and accessible in UI help and docs site

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Published on public docs site

Implement cost anomaly detection algorithms

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop algorithms to detect cost spikes and anomalies post-deployment by analyzing billing and cost attribution data. Support configurable thresholds and anomaly scoring. Store anomalies in a dedicated table.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 4.5h (1.1x factor)
• AI tools: GitHub Copilot
• AI category: complex_algorithm (medium confidence)

Acceptance Criteria:

• System detects cost spikes exceeding configurable thresholds
• Anomalies scored and stored in anomalies table
• Detection algorithms process data efficiently

Definition of Done:

• Algorithms implemented and reviewed
• Unit and integration tests cover detection logic
• Performance benchmarks meet requirements

Implement cloud provider billing API clients for AWS, GCP, and Azure

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop secure API clients to authenticate and fetch billing data from AWS Cost Explorer, GCP Billing API, and Azure Consumption API. Handle authentication, pagination, rate limiting, and error retries.
• Estimate: 7 hours
• Story Points: 5
• Traditional estimate: 7h | AI-accelerated: 5h (1.4x factor)
• AI tools: Amazon Q Developer, Gemini Code Assist
• AI category: implementation (high confidence)

Acceptance Criteria:

• Clients authenticate securely with each cloud provider
• Billing data imported on configurable schedule
• Clients handle pagination, rate limits, and retries

Definition of Done:

• Clients implemented and reviewed
• Integration tests with mock APIs
• Documentation of API usage and configuration

Design and implement billing_imports database table and data ingestion pipeline

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Create PostgreSQL 16 schema for billing_imports table to store raw imported billing data securely. Implement ingestion pipeline to process and persist billing data from cloud provider clients.
• 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:

• billing_imports table created with appropriate columns and constraints
• Billing data ingested and stored securely
• Data ingestion pipeline handles errors and retries

Definition of Done:

• Migration scripts tested and applied
• Ingestion service implemented and tested
• Integration tests verify data persistence

Write unit and integration tests for billing API clients and ingestion pipeline

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Develop unit tests for cloud billing API clients covering authentication, pagination, and error handling. Write integration tests for ingestion pipeline ensuring data is correctly stored in billing_imports table.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover API clients with 80% coverage
• Integration tests validate ingestion and storage
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated
• Test failures block CI pipeline

Design and implement cost attribution algorithms

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop algorithms to map cloud billing records to Kubernetes workloads using cluster metadata, resource labels, and namespaces. Store attribution results in cost_attributions table. Ensure processing completes within 1 hour batch window.
• Estimate: 7 hours
• Story Points: 5
• Traditional estimate: 7h | AI-accelerated: 6h (1.1x factor)
• AI tools: GitHub Copilot
• AI category: complex_algorithm (medium confidence)

Acceptance Criteria:

• Cost attributions link billing records to cluster and workload identifiers
• Attribution data stored in cost_attributions table
• Attribution processing completes within 1 hour batch window

Definition of Done:

• Algorithms implemented and reviewed
• Unit and integration tests cover attribution logic
• Performance benchmarks meet batch window

Implement API endpoint GET /api/v1/cost_attributions for querying attribution results

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop REST API endpoint to query cost attribution results by cluster, workload, and time range. Enforce OIDC JWT authentication and RBAC. Ensure response times under 200ms under normal load.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.4h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• API returns cost attribution data filtered by query parameters
• Enforces authentication and RBAC
• Response time under 200ms under normal load

Definition of Done:

• API implemented and reviewed
• Unit and integration tests cover endpoint
• API documented and tested

Write unit and integration tests for cost attribution algorithms and API

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Develop unit tests for cost attribution algorithms covering mapping logic and edge cases. Write integration tests for API endpoint verifying authentication, filtering, and data correctness.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.3h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover attribution logic with 80% coverage
• Integration tests verify API functionality and security
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated
• Test failures block CI pipeline

Implement alerting integration for cost anomalies

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop integration with monitoring and alerting systems to send notifications via UI, email, and Slack when cost anomalies are detected. Support configurable alert channels and thresholds.
• Estimate: 2 hours
• Story Points: 2
• Traditional estimate: 2h | AI-accelerated: 1.4h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• Alerts sent via UI notifications, email, and Slack
• Alert channels and thresholds configurable
• Alerting integrates with monitoring and logging systems

Definition of Done:

• Alerting implemented and tested
• Integration tests verify alert delivery
• Documentation updated with alerting setup

Write unit and integration tests for cost anomaly detection and alerting

• Type: testing | Priority: high | Status: todo
• Critical path: No
• Description: Develop unit tests for anomaly detection algorithms and alerting logic. Write integration tests to verify alert delivery via UI, email, and Slack. Ensure 80% coverage and CI integration.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.7h (1.4x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover detection and alerting logic with 80% coverage
• Integration tests verify alert delivery
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated
• Test failures block CI pipeline

Write unit and integration tests for Kubernetes agent core and Helm chart

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 2aa37a9c-2b47-4eea-9c26-8e7a05f88cc2, e62c4709-6167-4755-b02a-ccf63d551d36
• Critical path: No
• Description: Develop comprehensive unit tests for the agent's cluster metadata collection and RBAC enforcement logic. Write integration tests to validate Helm chart installation, upgrade, and configuration of TLS and RBAC settings.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover at least 80% of agent code
• Integration tests validate Helm chart installation and upgrades
• Tests run successfully in CI pipeline

Definition of Done:

• Tests implemented and pass reliably
• Coverage reports generated and reviewed
• Test failures block CI pipeline

Document cost anomaly detection and alerting features

• Type: documentation | Priority: high | Status: todo
• Critical path: No
• Description: Write documentation describing the cost anomaly detection algorithms, configuration options, alerting channels, and troubleshooting. Include examples of alert notifications and UI integration.
• Estimate: 1 hours
• Story Points: 1
• Traditional estimate: 1h | AI-accelerated: 0.6h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation explains detection algorithms and alerting setup
• Includes configuration and troubleshooting sections
• Published and accessible on docs site

Definition of Done:

• Documentation reviewed and approved
• Examples tested and verified
• Published on public docs site

Create React component to display promotion risk assessment in UI

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop React 19 component to show risk level and detailed risk factors for each promotion in the promotion preview UI. Integrate with backend risk assessment API and handle loading and error states.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2.86h (1.4x factor)
• AI tools: Bolt.new
• AI category: implementation (high confidence)

Acceptance Criteria:

• UI displays risk level with color-coded indicators
• Risk factors are listed with descriptions
• Component fetches risk data securely from backend API

Definition of Done:

• Risk assessment UI component implemented and integrated
• Unit tests cover 80% of component code
• Code review and merge completed

Design and implement workload diff computation service

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend service module to fetch read-only cluster state from Kubernetes API and agent snapshots, compute diffs of Kubernetes workloads between source (staging) and target (production) namespaces, and store results in PostgreSQL workload_diffs table linked to promotion IDs.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.57h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• Diff computation completes within 5 seconds for typical workloads
• Diff results stored in workload_diffs table with correct promotion linkage
• Service correctly fetches cluster state from Kubernetes API and agent snapshots

Definition of Done:

• Diff computation service implemented and integrated
• Unit and integration tests cover 80% of new code
• Code review completed and merged

Develop React component for workload diff preview UI

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement a React 19 component to display added, removed, and modified Kubernetes resources in a clear diff format between staging and production namespaces. Integrate with backend API to fetch diff data and render with Material UI components.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2.86h (1.4x factor)
• AI tools: Bolt.new
• AI category: implementation (high confidence)

Acceptance Criteria:

• UI displays workload diffs with clear visual indicators for added, removed, and modified resources
• Component fetches diff data from backend API and handles loading and error states
• UI updates within 1 second of data fetch completion

Definition of Done:

• Diff preview component implemented and integrated in promotion preview page
• Unit tests with Jest and React Testing Library cover 80% of component code
• Code review completed and merged

Implement RiskAnalyzer service for promotion risk assessment

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend RiskAnalyzer microservice to compute risk levels (low, medium, high, critical) and risk factors (resource quota breaches, environment drift, policy violations) based on cluster state and promotion diffs. Store risk data in promotion_risks table linked to promotions. Ensure API response times under 5 seconds.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.57h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• RiskAnalyzer computes risk levels and factors accurately
• Risk data stored in promotion_risks table linked to promotion
• Risk assessment API responds within 5 seconds

Definition of Done:

• RiskAnalyzer service implemented and integrated
• Unit and integration tests cover 80% of new code
• Code review and merge completed

Implement cost estimation engine integration with promotion previews

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend service to aggregate cluster state and billing data to estimate baseline, projected, and delta monthly costs in USD for workload promotions. Store cost preview data in promotion_costs table linked to promotions. Ensure API response times under 5 seconds (95th percentile).
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.57h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• Cost preview API returns baseline, projected, and delta monthly costs in USD
• Cost preview data stored correctly in promotion_costs table linked to promotion
• API response time is under 5 seconds for 95th percentile of requests

Definition of Done:

• Cost estimation engine implemented and integrated
• Unit and integration tests cover 80% of new code
• Code review and merge completed

Develop UI components to display Git PR URL and status in promotion details

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement React 19 components to show Git pull request URLs and current status for each promotion in the promotion preview UI. Integrate with backend APIs to fetch Git metadata securely.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2.14h (1.4x factor)
• AI tools: Bolt.new
• AI category: implementation (high confidence)

Acceptance Criteria:

• UI displays Git PR URL as clickable link with correct status indicator
• Component fetches Git metadata from backend API with JWT authentication
• UI updates PR status in real-time or on refresh

Definition of Done:

• Git PR display components implemented and integrated
• Unit tests cover 80% of new UI code
• Code review and merge completed

Create React component to display cost impact in promotion preview UI

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Implement React 19 component to show baseline, projected, and delta monthly costs in USD along with resource usage deltas (CPU, memory). Integrate with backend cost preview API and handle loading and error states.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2.86h (1.4x factor)
• AI tools: Bolt.new
• AI category: implementation (high confidence)

Acceptance Criteria:

• UI displays cost preview data with clear formatting and units
• Component fetches data securely from backend API with JWT authentication
• UI updates within 1 second of data fetch completion

Definition of Done:

• Cost impact component implemented and integrated in promotion preview page
• Unit tests cover 80% of component code
• Code review completed and merged

Write unit and integration tests for cost estimation backend and UI components

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 02cdcf7b-f9b8-4da0-b305-80f4eef94771, 0d561f83-ff57-4c58-af90-73f8884d1097
• Critical path: No
• Description: Develop unit tests for cost estimation service and React cost impact preview component. Write integration tests to verify API correctness and UI rendering with mock data. Ensure 80% code coverage and CI integration.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover at least 80% of backend and frontend code
• Integration tests verify API responses and UI rendering
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Document Git provider integration and authentication setup

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): d7acd801-f2e9-4e4c-9183-c42ce82a21c6
• Critical path: No
• Description: Write detailed documentation describing how to configure Git provider authentication (OAuth2, PAT), API usage for commit and PR creation, error handling, and rate limit management. Include configuration examples and troubleshooting tips.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation includes authentication setup and API usage
• Examples and troubleshooting tips provided
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Document workload diff preview API and UI usage

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): 0be3362c-1e3f-4098-a1e3-a6b12d09dcc0, eae2a925-3bf6-4c60-8ec1-2d051b0b5aee
• Critical path: No
• Description: Write detailed API documentation for workload diff endpoints, including request/response schemas and authentication. Create user guide documentation describing how to use the workload diff preview UI, interpret diff results, and troubleshoot common issues.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• API documentation includes example requests and responses for diff preview endpoints
• User guide explains UI features and how to interpret workload diffs
• Documentation reviewed and published in docs site

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Implement GitOps Orchestrator integration with GitHub, GitLab, and Bitbucket APIs

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend service to authenticate with Git providers using OAuth2 or PAT, create commits and pull requests in target repositories for promotion workflows. Store Git metadata in git_repos, git_commits, and git_pull_requests tables. Handle API rate limits and errors with retries.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.57h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• System authenticates with Git providers using OAuth2 or PAT
• Commits and pull requests created successfully in target repos
• Git metadata stored correctly in database tables

Definition of Done:

• GitOps Orchestrator Git integration implemented
• Unit and integration tests cover 80% of new code
• Code review and merge completed

Implement AuditLogService to record immutable promotion Git events

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend AuditLogService to record immutable entries for all promotion-related Git commits and pull requests. Ensure audit logs include user and timestamp metadata, are tamper-evident, exportable, and stored in PostgreSQL with indexing for fast queries.
• Estimate: 4 hours
• Story Points: 2
• Traditional estimate: 4h | AI-accelerated: 2.86h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• Audit logs include user and timestamp metadata for all promotion Git events
• Audit logs are immutable and tamper-evident
• Audit logs are exportable and indexed for fast queries

Definition of Done:

• AuditLogService implemented and integrated
• Unit and integration tests cover 80% of new code
• Code review and merge completed

Document conflict detection and resolution features

• Type: documentation | Priority: medium | Status: todo
• Depends on (complete these first): e324eae9-dc0d-490e-bf01-7dd50f57caa0
• Critical path: No
• Description: Write documentation describing conflict detection mechanisms, UI alerts, manual resolution workflows, and audit logging. Include user guidance and troubleshooting tips.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers conflict detection and resolution workflows
• Includes UI usage and troubleshooting
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Document promotion risk assessment API and UI features

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): 9d86cfad-2a1b-4d77-adad-d6195d423b17, f4995955-8d20-427d-861f-a3bf45c362f2
• Critical path: No
• Description: Write API documentation for risk assessment endpoints including request/response schemas and authentication. Create user guide documentation explaining risk levels, factors, and UI interpretation. Include troubleshooting tips.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• API docs include example requests and responses for risk endpoints
• User guide explains risk assessment UI and interpretation
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Design and implement Promotion Preview REST and gRPC API endpoints

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop RESTful and gRPC API endpoints for promotion preview creation (POST /api/v1/promotions/preview), retrieval (GET /api/v1/promotions/:id), approval and rejection (PATCH /api/v1/promotions/:id/approve, /reject). Enforce OIDC JWT authentication and RBAC authorization. Ensure API responses conform to JSON schema and error handling standards.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 4.29h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• POST /api/v1/promotions/preview accepts valid workload promotion preview requests and returns 201 status
• GET /api/v1/promotions/:id returns promotion details including diff, cost, risk, and explanation
• PATCH endpoints update promotion status and trigger GitOps actions with proper authorization

Definition of Done:

• API endpoints implemented and integrated
• Unit and integration tests cover 80% of API code
• API documentation generated and reviewed

Implement Git provider authentication and API integration

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend modules to authenticate with GitHub, GitLab, and Bitbucket using OAuth2 or PAT. Implement API clients to create commits and pull requests. Handle API rate limits and errors with retry logic. Store Git metadata in database tables.
• Estimate: 5 hours
• Story Points: 3
• Traditional estimate: 5h | AI-accelerated: 3.57h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• System authenticates successfully with all supported Git providers
• Commits and pull requests can be created via APIs
• API rate limits and errors handled gracefully with retries

Definition of Done:

• Git provider integration implemented
• Unit and integration tests cover 80% of new code
• Code review and merge completed

Write unit and integration tests for AuditLogService

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): a0c32583-3ece-4d05-985a-45d15e744333
• Critical path: No
• Description: Develop unit tests for AuditLogService functionality including event recording, immutability, and export. Write integration tests verifying audit log storage, indexing, and query performance. Ensure 80% code coverage and CI integration.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.33h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover 80% of AuditLogService code
• Integration tests verify audit log persistence and query
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Implement automatic Git commit and pull request creation on promotion approval

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend logic to trigger automatic Git commit with workload changes and create pull requests with appropriate title and description upon promotion approval. Ensure PR links back to promotion record in UI. Implement conflict detection to prevent duplicate or conflicting PRs. Reflect PR status updates in promotion status.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 4.29h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• Promotion approval triggers Git commit and PR creation automatically
• PR includes correct title, description, and links to promotion
• Conflict detection prevents duplicate or conflicting PRs

Definition of Done:

• Automatic commit and PR creation implemented
• Unit and integration tests cover 80% of new code
• Code review and merge completed

Document cost estimation API and UI usage

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): 02cdcf7b-f9b8-4da0-b305-80f4eef94771, 0d561f83-ff57-4c58-af90-73f8884d1097
• Critical path: No
• Description: Write API documentation for cost preview endpoints including request/response schemas and authentication. Create user guide documentation explaining cost preview UI features, interpretation of cost data, and troubleshooting.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• API docs include example requests and responses for cost preview endpoints
• User guide explains cost preview UI and data interpretation
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Implement conflict detection and resolution in GitOps workflows

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Develop backend logic to detect merge conflicts before PR creation using Git merge checks and diff analysis. Implement UI alerts to notify users of conflicts with actionable messages. Support manual conflict resolution via UI or CLI. Log conflicts in audit trail.
• Estimate: 6 hours
• Story Points: 3
• Traditional estimate: 6h | AI-accelerated: 4.29h (1.4x factor)
• AI tools: Devin (Cognition)
• AI category: implementation (high confidence)

Acceptance Criteria:

• System detects merge conflicts before PR creation reliably
• UI alerts users with clear conflict messages and resolution options
• Conflicts are logged in audit trail with relevant metadata

Definition of Done:

• Conflict detection and resolution implemented
• Unit and integration tests cover 80% of new code
• Code review and merge completed

Write unit and integration tests for workload diff service and UI

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 0be3362c-1e3f-4098-a1e3-a6b12d09dcc0, eae2a925-3bf6-4c60-8ec1-2d051b0b5aee
• Critical path: No
• Description: Develop comprehensive unit tests for backend diff computation service and React workload diff preview component. Include integration tests for API endpoints and UI data fetching. Ensure 80% code coverage and integration with CI pipeline.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover at least 80% of diff service and UI component code
• Integration tests verify API responses and UI rendering with mock data
• Tests run successfully in CI pipeline with no failures

Definition of Done:

• Test suites implemented and passing
• Code coverage reports generated and reviewed
• Test documentation updated

Write unit and integration tests for GitOps orchestrator and UI Git PR components

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 18c7c8f4-9e2a-49d1-8e35-b61603c1c0a2, 65d5ea86-3cd4-4ffc-9e4d-f7f750efc753
• Critical path: No
• Description: Develop unit tests for GitOps orchestrator backend service and React Git PR status components. Write integration tests to verify API correctness and UI rendering. Ensure 80% coverage and CI integration.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover at least 80% of backend and frontend code
• Integration tests verify API responses and UI rendering
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Document GitOps orchestrator Git provider integration and UI Git PR features

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): 18c7c8f4-9e2a-49d1-8e35-b61603c1c0a2, 65d5ea86-3cd4-4ffc-9e4d-f7f750efc753
• Critical path: No
• Description: Write API documentation for GitOps orchestrator endpoints related to Git provider integration, including authentication and PR creation. Document UI features displaying Git PR URLs and statuses. Include troubleshooting and usage examples.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• API docs include authentication and PR creation details
• User guide explains UI Git PR display and interpretation
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Write unit and integration tests for RiskAnalyzer service and risk UI components

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 9d86cfad-2a1b-4d77-adad-d6195d423b17, f4995955-8d20-427d-861f-a3bf45c362f2
• Critical path: No
• Description: Develop unit tests for RiskAnalyzer backend service and React risk assessment components. Write integration tests to verify API correctness and UI rendering. Ensure 80% code coverage and CI integration.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover at least 80% of backend and frontend code
• Integration tests verify API responses and UI rendering
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Write unit and integration tests for Promotion Preview API endpoints

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 0db4758c-e112-470b-b0b0-3a0fb2d211ed
• Critical path: No
• Description: Develop unit tests for REST and gRPC promotion preview endpoints covering creation, retrieval, approval, and rejection. Write integration tests verifying authentication, RBAC, and error handling. Ensure 80% code coverage and CI integration.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover at least 80% of API code
• Integration tests verify authentication and RBAC enforcement
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Document Promotion Preview API endpoints and usage

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): 0db4758c-e112-470b-b0b0-3a0fb2d211ed
• Critical path: No
• Description: Write comprehensive API documentation for promotion preview endpoints including request/response schemas, authentication, error codes, and examples. Include usage guidelines for clients and integration instructions.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• API docs include detailed request and response examples
• Authentication and error handling documented
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Write unit and integration tests for Git provider integration

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): d7acd801-f2e9-4e4c-9183-c42ce82a21c6
• Critical path: No
• Description: Develop unit tests for authentication and API client modules. Write integration tests simulating Git provider API interactions, including error and rate limit handling. Ensure 80% code coverage and CI integration.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover 80% of Git provider integration code
• Integration tests simulate API calls and error scenarios
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Write unit and integration tests for automatic commit and PR creation

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 9c8bb25c-1eb3-4561-8896-86bfda223a8d
• Critical path: No
• Description: Develop unit tests for automatic commit and pull request creation logic. Write integration tests simulating promotion approval flows and Git provider interactions. Ensure 80% coverage and CI integration.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover 80% of auto commit and PR creation code
• Integration tests verify promotion approval triggers and Git interactions
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Document automatic commit and pull request creation workflow

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): 9c8bb25c-1eb3-4561-8896-86bfda223a8d
• Critical path: No
• Description: Write documentation describing the automatic commit and PR creation process triggered by promotion approval. Include API usage, conflict detection behavior, and UI integration for PR status display. Provide troubleshooting and configuration guidance.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation includes detailed workflow and API descriptions
• Examples and troubleshooting tips provided
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Document audit trail features and usage for promotion Git events

• Type: documentation | Priority: high | Status: todo
• Depends on (complete these first): a0c32583-3ece-4d05-985a-45d15e744333
• Critical path: No
• Description: Write detailed documentation describing audit trail capabilities for promotion Git commits and PRs, including immutability, export formats, query interfaces, and compliance considerations. Provide usage examples and troubleshooting tips.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.18h (1.7x factor)
• AI tools: Claude Code (Anthropic)
• AI category: documentation (high confidence)

Acceptance Criteria:

• Documentation covers audit trail features and compliance
• Includes examples of audit log queries and exports
• Documentation published and reviewed

Definition of Done:

• Documentation written and peer-reviewed
• Docs integrated into documentation site
• Examples tested for accuracy

Write unit and integration tests for conflict detection and UI alerts

• Type: testing | Priority: medium | Status: todo
• Depends on (complete these first): e324eae9-dc0d-490e-bf01-7dd50f57caa0
• Critical path: No
• Description: Develop unit tests for conflict detection backend service and React ConflictAlert component. Write integration tests verifying conflict detection accuracy and UI alert rendering. Ensure 80% code coverage and CI integration.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover 80% of conflict detection and UI code
• Integration tests verify conflict scenarios and UI alerts
• Tests pass successfully in CI pipeline

Definition of Done:

• Tests implemented and passing
• Coverage reports generated
• Test documentation updated

Implement LLM API integration module for explanation generation

• Type: development | Priority: high | Status: todo
• Critical path: No
• Description: Develop backend service module in Go to send sanitized promotion metadata to OpenAI/Azure OpenAI LLM API, handle authentication with securely stored API keys, implement rate limiting, retries on failure, and cache explanations in Redis to reduce API calls.
• Estimate: 4 hours
• Story Points: 3
• Traditional estimate: 4h | AI-accelerated: 2.85h (1.4x factor)
• AI tools: GitHub Copilot, Cursor
• AI category: implementation (high confidence)

Acceptance Criteria:

• LLM API integration sends sanitized promotion metadata correctly
• API keys are securely stored and usage is monitored
• Rate limiting and retry logic handle API errors gracefully

Definition of Done:

• Code implemented and integrated with LLM API
• Unit and integration tests cover 80% of new code
• Code reviewed and merged
• Documentation updated with API integration details

Implement backend APIs for Helm release and chart management

• Type: development | Priority: medium | Status: todo
• Critical path: No
• Description: Develop REST API endpoints in Go to list Helm releases per cluster, list available Helm charts, and add new Helm charts. Enforce OIDC JWT authentication and RBAC authorization. Ensure response times under 200ms under normal load.
• Estimate: 3 hours
• Story Points: 2
• Traditional estimate: 3h | AI-accelerated: 2.14h (1.4x factor)
• AI tools: GitHub Copilot
• AI category: implementation (high confidence)

Acceptance Criteria:

• GET /api/v1/clusters/:cluster_id/helm/releases returns correct Helm release data
• GET /api/v1/helm/charts returns available Helm charts
• POST /api/v1/helm/charts supports adding new charts with validation

Definition of Done:

• API endpoints implemented and integrated
• Unit and integration tests cover 80% of code
• Code reviewed and merged

Write unit and integration tests for LLM API integration and caching

• Type: testing | Priority: high | Status: todo
• Depends on (complete these first): 8a987603-925c-4b3c-92e2-9554ab0456ee
• Critical path: No
• Description: Develop comprehensive unit tests for LLM API client, including authentication, request/response handling, error retries, and Redis caching. Write integration tests simulating promotion explanation generation and cache hits.
• Estimate: 2 hours
• Story Points: 1
• Traditional estimate: 2h | AI-accelerated: 1.33h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)

Acceptance Criteria:

• Unit tests cover 80% of LLM API integration code
• Integration tests verify caching reduces redundant API calls
• Tests pass in CI pipeline with no failures

Definition of Done:

• Tests implemented and passing
• Coverage reports show 80%+ coverage
• Test cases cover error and edge scenarios
• Tests integrated into CI pipeline

Write unit and integration tests for Helm management APIs

• Type: testing | Priority: medium | Status: todo
• Depends on (complete these first): 351d8976-8904-42cc-9ec9-1fd7b9910fbb
• Critical path: No
• Description: Develop unit tests for Helm API handlers and services, including authentication, RBAC enforcement, and data validation. Write integration tests to verify API responses and error handling under normal and edge cases.
• Estimate: 1.5 hours
• Story Points: 1
• Traditional estimate: 1.5h | AI-accelerated: 1h (1.5x factor)
• AI tools: Windsurf (Codeium)
• AI category: test_generation (high confidence)