MULTI-AGENT EXECUTION PLAN - CODITECT License Management Platform

Date: November 30, 2025 Status: ACTIVE EXECUTION Version: 1.0 Goal: Backend MVP in 2-3 days with parallel documentation completion

Executive Summary

Current State: 47/120 tasks complete (41%), Backend MVP 68% complete Critical Blocker: Redis Memorystore NOT deployed (30 min fix) Accelerated Path: Backend MVP in 2-3 days (was 4-5 days) Key Discovery: Django backend 60% complete with production-ready code!

Multi-Track Strategy:

Track 1 (Critical Path): Backend MVP → 2-3 days → P0 Priority
Track 2 (Parallel): Phase 7 Documentation → 6-7 days → P1 Priority

Success Metrics:

Backend MVP deployed to GKE with SSL + DNS
All license endpoints working (acquire, heartbeat, release)
80%+ test coverage
Phase 7 documentation complete (all FastAPI→Django conversions)

1. Current State Assessment

Completed Work (47/120 tasks, 41%)

Phase 0: Infrastructure ✅ 95% (19/20 tasks)

GKE cluster operational (3 nodes, us-central1)
Cloud SQL PostgreSQL 16 with regional HA
Cloud KMS deployed (RSA-4096 license signing ready)
VPC networking with private subnets + Cloud NAT
Secret Manager configured (9 secrets)
Documentation organized (100/100 CODITECT standards)
17 comprehensive diagrams created

Phase 1: Django Backend ✅ 60% (8/13 tasks)

Models complete (343 lines) - License + Session with multi-tenant
Serializers complete (163 lines) - Full validation
Views complete (212 lines) - Redis Lua atomic seat acquisition
Settings configured - DRF, django-multitenant, CORS, Celery

Phase 7: Documentation ✅ 60% (20/35 tasks)

Phase 7A: 6/6 complete (Django reference diagrams)
Phase 7B: 8/8 complete (Core sequence diagrams + ADRs)
Phase 7C: 4/8 complete (Supporting sequence diagrams)
Phase 7D: 0/7 pending (Remaining C3/C4 diagrams)
Phase 7E: 2/6 complete (Validation and gap analysis)

Critical Blocker Discovered

Redis Memorystore NOT Deployed 🚨

Status: ❌ Missing (blocks ALL backend development) Impact: views.py requires Redis for atomic seat counting Fix Time: 30 minutes Priority: P0 - MUST BE DONE FIRST

cd opentofu/environments/dev
tofu apply -target=module.redis
gcloud redis instances list --region=us-central1

Acceptance Criteria:

Redis operational with 6GB capacity
Accessible from GKE via private IP (10.0.0.3:6379)
RDB persistence enabled
Connection tested from GKE pod

Missing Components (40% of Backend)

Services Layer (2-3 hours):

backend/licenses/services.py - Business logic wrapper
Cloud KMS integration for license signing
Celery tasks for zombie session cleanup

Tests (4-5 hours):

pytest suite with 80%+ coverage
Model tests (License, Session methods)
View tests (API endpoints)
Service tests (business logic)
Redis Lua script tests

Containerization (3 hours):

Dockerfile (Django + Gunicorn)
Kubernetes manifests (Deployment, Service, HPA)
ConfigMap and Secret resources

Deployment (2 hours):

Build and push Docker image
Apply K8s manifests to GKE
Verify 3 pods running
Health endpoint checks

2. Critical Path Validation

Backend MVP Critical Path

Redis Deployment (30 min) ⬅️ BLOCKER
    ↓
Services Layer (2-3h)
    ↓
Test Suite (4-5h)
    ↓
Docker + K8s (3h)
    ↓
GKE Deployment (2h)
    ↓
Backend MVP Complete ✅

Total Time: 11.5-13.5 hours (2-3 days at 4-5 hours/day)

Validation:

✅ Redis is ONLY blocker
✅ All other infrastructure deployed
✅ Django code 60% complete
✅ Cloud KMS already deployed (unexpected win!)
✅ No external dependencies

Phase 7 Deferred Work (NOT Blocking MVP)

15 tasks (30 hours) deferred to parallel track:

4 tasks Phase 7C (8 hours) - C4 diagrams, security diagram
7 tasks Phase 7D (15 hours) - C3 diagrams, supporting diagrams
4 tasks Phase 7E (6.5 hours) - Code validation, cross-reference index

Key Insight: Phase 7 documentation does NOT block backend MVP. Strategy: Technical writer can complete Phase 7 in parallel with backend development.

3. Resource Allocation Strategy

Track 1: Backend MVP (Critical Path) - P0

Resource: 1 Backend Developer (full-time) Duration: 2-3 days Focus: Services, tests, deployment Priority: P0 - CRITICAL

Day-by-Day Breakdown:

Day 1 (Today - 4-5 hours):

Deploy Redis Memorystore (30 min) - BLOCKER REMOVAL
Create Services layer (2-3 hours)
Start test suite (1-2 hours)

Day 2 (Tomorrow - 5-6 hours): 4. Complete tests (3-4 hours) 5. Create Dockerfile (1 hour) 6. Create Kubernetes manifests (2 hours)

Day 3 (4-6 hours): 7. Deploy to GKE (2-3 hours) 8. Configure SSL + DNS (2-3 hours) 9. End-to-end testing

Track 2: Phase 7 Documentation (Parallel) - P1

Resource: 1 Technical Writer (part-time) Duration: 6-7 days Focus: Remaining diagrams and documentation Priority: P1 - Important but not blocking

Week 1 (Days 1-5):

Create 4 deferred Phase 7C diagrams (8 hours)
Create 7 Phase 7D diagrams/docs (15 hours)

Week 2 (Days 6-7):

Complete Phase 7E validation (6.5 hours)
Final gap analysis and reporting

Parallel Execution: These tracks run independently:

Backend developer focuses on MVP (Days 1-3)
Technical writer completes Phase 7 (Days 1-7)
No coordination required (zero dependencies)

4. Multi-Agent Coordination Plan

Phase 1: Immediate Actions (Day 1 - 4-5 hours)

Agent 1: DevOps Engineer (30 min)

Task: Deploy Redis Memorystore Priority: P0 - CRITICAL BLOCKER Agent Type: codi-devops-engineer

Invocation:

Task(
    subagent_type="codi-devops-engineer",
    description="Deploy Redis Memorystore to unblock backend development",
    prompt="""Deploy Redis Memorystore 6GB BASIC tier:

Steps:
1. cd opentofu/environments/dev
2. tofu apply -target=module.redis
3. Verify: gcloud redis instances list --region=us-central1
4. Test connectivity from GKE pod

Acceptance Criteria:
- Redis operational with 6GB capacity
- Accessible via private IP: 10.0.0.3:6379
- RDB persistence enabled
- Connection tested successfully

Configuration:
- Tier: BASIC
- Memory: 6GB
- Region: us-central1
- Network: projects/coditect-cloud-infra/global/networks/coditect-vpc
- Connect Mode: PRIVATE_SERVICE_ACCESS

Verification:
kubectl run redis-test --rm -i --tty --image redis:7-alpine -- redis-cli -h 10.0.0.3 PING
# Should return PONG
"""
)

Agent 2: Backend Developer (2-3 hours)

Task: Create Services Layer Priority: P0 - CRITICAL PATH Agent Type: codi-backend-engineer Dependencies: Redis deployed

Invocation:

Task(
    subagent_type="codi-backend-engineer",
    description="Create Django services layer with Cloud KMS integration",
    prompt="""Create backend/licenses/services.py:

Reference existing code:
- Models: backend/licenses/models.py (343 lines)
- Views: backend/licenses/views.py (212 lines - contains Lua scripts)
- Serializers: backend/licenses/serializers.py (163 lines)

Requirements:

1. LicenseService class:
   - acquire_license(user, license_key, hardware_id)
     * Validate license exists and is active
     * Redis atomic seat check (use Lua script from views.py)
     * Create session in PostgreSQL
     * Sign license data with Cloud KMS (RSA-4096)
     * Return signed token + session ID

   - release_license(session_id)
     * Mark session as released
     * Decrement Redis counter
     * Update seat utilization

   - cleanup_zombie_sessions() [Celery task]
     * Query sessions past 6-min TTL
     * Release expired sessions
     * Update Redis counters

2. Cloud KMS Integration:
   - Use existing keyring: projects/coditect-cloud-infra/locations/us-central1/keyRings/coditect-license-keys
   - Sign license data with RSA-4096
   - Return signature for client verification

3. Unit tests:
   - test_acquire_license_success()
   - test_acquire_license_no_seats()
   - test_release_license_success()
   - test_cloud_kms_signing() [mocked]
   - test_cleanup_zombie_sessions()

Acceptance Criteria:
- Services layer wraps views.py logic
- Cloud KMS signing functional
- Unit tests pass with 80%+ coverage
- Code follows Django best practices

Files to create:
- backend/licenses/services.py (~300 lines)
- backend/licenses/tasks.py (Celery tasks, ~100 lines)
- backend/licenses/tests/test_services.py (~200 lines)
"""
)

Agent 3: QA Engineer (1-2 hours)

Task: Start Test Suite Priority: P0 - CRITICAL PATH Agent Type: codi-testing-specialist Dependencies: Services layer complete

Invocation:

Task(
    subagent_type="codi-testing-specialist",
    description="Create comprehensive pytest suite for Django backend",
    prompt="""Create test suite in backend/licenses/tests/:

Reference existing code:
- Models: backend/licenses/models.py (343 lines)
- Serializers: backend/licenses/serializers.py (163 lines)
- Views: backend/licenses/views.py (212 lines)
- Services: backend/licenses/services.py (to be created)

Test Structure:

1. test_models.py:
   - test_license_is_valid()
   - test_license_has_available_seats()
   - test_license_get_seat_utilization()
   - test_session_is_heartbeat_alive()
   - test_session_record_heartbeat()
   - test_session_release()

2. test_serializers.py:
   - test_license_acquire_request_validation()
   - test_heartbeat_request_validation()
   - test_release_request_validation()

3. test_views.py (to be completed Day 2):
   - test_acquire_license_success()
   - test_acquire_license_no_seats()
   - test_acquire_license_invalid_key()
   - test_heartbeat_success()
   - test_heartbeat_expired_session()
   - test_release_license_success()

4. conftest.py:
   - Fixtures for test database
   - Redis mock fixtures
   - Cloud KMS mock fixtures
   - Test tenant and user fixtures

Acceptance Criteria:
- All model and serializer tests complete
- Tests use pytest best practices
- Fixtures properly set up
- Mock external dependencies (Redis, Cloud KMS)
- Tests pass with pytest -v

Target: 80%+ coverage on models and serializers
"""
)

Phase 2: Complete Backend Implementation (Day 2 - 5-6 hours)

Agent 4: QA Engineer (3-4 hours)

Task: Complete Test Suite Priority: P0 Agent Type: codi-testing-specialist

Invocation:

Task(
    subagent_type="codi-testing-specialist",
    description="Complete Django backend test suite with 80%+ coverage",
    prompt="""Complete test suite in backend/licenses/tests/:

1. Complete test_views.py:
   - All API endpoint tests
   - Multi-tenant isolation tests
   - Redis Lua script tests
   - Error handling tests

2. Add test_services.py:
   - Service layer business logic tests
   - Cloud KMS integration tests (mocked)
   - Celery task tests (mocked)

3. Integration tests:
   - test_integration.py
   - End-to-end license acquisition flow
   - End-to-end heartbeat flow
   - End-to-end release flow

4. Run coverage report:
   pytest --cov=backend/licenses --cov-report=html

Acceptance Criteria:
- ≥80% test coverage
- All tests pass
- No warnings or deprecation errors
- Integration tests verify full workflows
- Coverage report generated

Target: 80-85% coverage on entire licenses app
"""
)

Agent 5: DevOps Engineer (1 hour)

Task: Create Dockerfile Priority: P0 Agent Type: codi-devops-engineer

Invocation:

Task(
    subagent_type="codi-devops-engineer",
    description="Create production-ready Dockerfile for Django backend",
    prompt="""Create backend/Dockerfile:

Requirements:
- Base: python:3.11-slim
- Dependencies: requirements.txt (Django 5.2.8, DRF 3.16.1, etc.)
- Server: Gunicorn with 4 workers
- Health check: /api/v1/health/
- Non-root user: django (UID 1000)

Dockerfile Structure:
1. Install system dependencies (PostgreSQL client, Redis client)
2. Create django user and workdir
3. Copy requirements.txt and install Python packages
4. Copy application code
5. Run migrations (python manage.py migrate)
6. Collect static files (python manage.py collectstatic)
7. Expose port 8000
8. CMD: gunicorn coditect_license.wsgi:application --bind 0.0.0.0:8000 --workers 4

Multi-stage build for smaller image:
- Stage 1: Build dependencies
- Stage 2: Production runtime

Test locally:
1. docker build -t coditect-backend:test .
2. docker run -p 8000:8000 coditect-backend:test
3. curl http://localhost:8000/api/v1/health/

Acceptance Criteria:
- Image builds successfully
- Image size <500MB
- Health endpoint responds 200 OK
- Connects to Cloud SQL via private IP
- No security vulnerabilities (scan with trivy)

Target: Production-ready Dockerfile with best practices
"""
)

Agent 6: DevOps Engineer (2 hours)

Task: Create Kubernetes Manifests Priority: P0 Agent Type: codi-devops-engineer

Invocation:

Task(
    subagent_type="codi-devops-engineer",
    description="Create Kubernetes deployment manifests for Django backend",
    prompt="""Create kubernetes/base/ manifests:

1. backend-deployment.yaml:
   - Deployment with 3 replicas
   - Rolling update strategy (maxSurge: 1, maxUnavailable: 0)
   - Container: gcr.io/coditect-cloud-infra/backend:latest
   - Resources: requests (256Mi, 0.5 CPU), limits (512Mi, 1 CPU)
   - Environment variables from ConfigMap
   - Secrets from Secret
   - Liveness probe: /api/v1/health/ (30s delay)
   - Readiness probe: /api/v1/ready/ (10s delay)

2. backend-service.yaml:
   - Service type: ClusterIP
   - Port: 80 → targetPort: 8000
   - Selector: app=backend

3. backend-hpa.yaml:
   - HorizontalPodAutoscaler
   - Min replicas: 2
   - Max replicas: 10
   - CPU target: 70%

4. backend-configmap.yaml:
   - Environment: dev
   - Debug: false
   - Allowed hosts: api.coditect.ai

5. backend-secrets.yaml:
   - Database URL: postgresql://...
   - Redis URL: redis://10.0.0.3:6379/0
   - Django secret key
   - Cloud KMS key path

Reference:
- GKE cluster: coditect-cluster (us-central1)
- Namespace: coditect-backend
- Cloud SQL private IP: 10.0.0.2:5432
- Redis private IP: 10.0.0.3:6379

Acceptance Criteria:
- All manifests valid (kubectl apply --dry-run)
- Secrets properly base64 encoded
- HPA configured for auto-scaling
- Probes configured for health monitoring

Target: Production-ready K8s manifests
"""
)

Phase 3: Deployment & Validation (Day 3 - 4-6 hours)

Agent 7: DevOps Engineer (2-3 hours)

Task: Deploy to GKE Priority: P0 Agent Type: codi-devops-engineer

Invocation:

Task(
    subagent_type="codi-devops-engineer",
    description="Deploy Django backend to GKE and verify operational",
    prompt="""Deploy backend to GKE:

1. Build Docker image:
   cd backend
   docker build -t gcr.io/coditect-cloud-infra/backend:v1.0.0 .
   docker push gcr.io/coditect-cloud-infra/backend:v1.0.0

2. Apply Kubernetes manifests:
   kubectl create namespace coditect-backend (if not exists)
   kubectl apply -k kubernetes/base/

3. Verify deployment:
   kubectl get pods -n coditect-backend
   kubectl get svc -n coditect-backend
   kubectl logs -n coditect-backend <pod-name>

4. Check pod health:
   kubectl exec -n coditect-backend <pod-name> -- curl localhost:8000/api/v1/health/

5. Verify connections:
   - Cloud SQL connection (check logs)
   - Redis connection (check logs)
   - Cloud KMS access (check logs)

6. Monitor pod status:
   kubectl get hpa -n coditect-backend
   kubectl top pods -n coditect-backend

Acceptance Criteria:
- 3 backend pods running
- All pods healthy (Ready 1/1)
- Health endpoints responding 200 OK
- Connected to Cloud SQL (check logs)
- Connected to Redis (check logs)
- Cloud KMS accessible (check logs)
- HPA operational (2-10 replicas)
- Logs streaming to Cloud Logging

Target: Fully operational backend in GKE
"""
)

Agent 8: DevOps Engineer (2-3 hours)

Task: Configure SSL + DNS Priority: P0 Agent Type: codi-devops-engineer

Invocation:

Task(
    subagent_type="codi-devops-engineer",
    description="Configure SSL certificate and DNS for api.coditect.ai",
    prompt="""Setup SSL + DNS for production access:

1. Create Ingress with Let's Encrypt:
   - kubernetes/base/backend-ingress.yaml
   - Annotations: cert-manager.io/cluster-issuer: letsencrypt-prod
   - TLS secret: api-coditect-tls
   - Host: api.coditect.ai
   - Backend service: backend-service:80

2. Install cert-manager (if not already):
   kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.13.0/cert-manager.yaml

3. Create ClusterIssuer:
   - letsencrypt-prod
   - Email: hal@az1.ai
   - ACME HTTP01 challenge

4. Configure DNS:
   - Domain: api.coditect.ai
   - Type: A
   - Value: <INGRESS_IP>
   - TTL: 300

5. Wait for certificate:
   kubectl get certificate -n coditect-backend
   kubectl describe certificate api-coditect-tls

6. Verify HTTPS:
   curl https://api.coditect.ai/api/v1/health/
   # Should return 200 OK with valid SSL

Acceptance Criteria:
- SSL certificate issued (Let's Encrypt)
- DNS resolving to Ingress IP
- HTTPS working (https://api.coditect.ai)
- HTTP redirects to HTTPS
- Valid SSL certificate (check with browser)

Target: Production-ready HTTPS endpoint
"""
)

Agent 9: QA Engineer (2-3 hours)

Task: End-to-End Testing Priority: P0 Agent Type: codi-testing-specialist

Invocation:

Task(
    subagent_type="codi-testing-specialist",
    description="Run end-to-end integration tests on deployed backend",
    prompt="""Test deployed backend at https://api.coditect.ai:

1. License Acquisition Test:
   curl -X POST https://api.coditect.ai/api/v1/licenses/acquire \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer <JWT_TOKEN>" \
     -d '{
       "license_key": "test-key-123",
       "hardware_id": "test-hardware-456",
       "user_email": "test@coditect.ai"
     }'

   Expected: 200 OK with session_id and signed license_data

2. Heartbeat Test:
   curl -X POST https://api.coditect.ai/api/v1/licenses/heartbeat \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer <JWT_TOKEN>" \
     -d '{
       "session_id": "<session_uuid>"
     }'

   Expected: 200 OK with updated expires_at

3. Release Test:
   curl -X POST https://api.coditect.ai/api/v1/licenses/release \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer <JWT_TOKEN>" \
     -d '{
       "session_id": "<session_uuid>"
     }'

   Expected: 200 OK with released_at timestamp

4. Atomic Seat Acquisition Test:
   - Create test license with seats_total=2
   - Acquire 2 seats (should succeed)
   - Try to acquire 3rd seat (should fail with 402 no_seats_available)
   - Release 1 seat
   - Acquire 3rd seat (should succeed)

5. Multi-Tenant Isolation Test:
   - Create 2 test tenants
   - Tenant A acquires license
   - Tenant B tries to access Tenant A's license (should fail 404)

6. Performance Test:
   - 100 concurrent license acquisitions
   - Verify all succeed
   - Verify Redis atomic operations maintain consistency

Acceptance Criteria:
- All endpoints working end-to-end
- Redis atomic operations verified
- Multi-tenant isolation enforced
- Performance acceptable (<500ms p95 latency)
- No errors in Cloud Logging

Target: Full end-to-end validation of Backend MVP
"""
)

Phase 4: Phase 7 Documentation (Parallel Track - Days 1-7)

Agent 10: Technical Writer (Days 1-5, 23 hours)

Task: Complete Phase 7C-7D Diagrams Priority: P1 - Parallel Work Agent Type: codi-documentation-writer

Invocation:

Task(
    subagent_type="codi-documentation-writer",
    description="Complete remaining Phase 7 diagrams and conversions",
    prompt="""Complete Phase 7C-7D documentation work:

Phase 7C Remaining (8 hours):
1. Create Diagram 15: Security Incident Response (2.5h)
   - Threat detection (Cloud Armor, Cloud Logging)
   - Alert routing (Cloud Monitoring → PagerDuty/Slack)
   - Incident handling workflow
   - Post-mortem process
   - Cross-reference with ADR-020

2. Create C4-02: License Acquisition Flow (2h)
   - Atomic Lua script flow
   - Django view processing
   - PostgreSQL RLS enforcement
   - Cross-reference with diagram 01

3. Create C4-03: JWT Authentication Flow (1.5h)
   - Django middleware chain
   - JWT token validation
   - Identity Platform integration
   - Cross-reference with ADR-020

4. Create C3-04: GKE Components (2h)
   - Kubernetes Pods (Django backend replicas)
   - Services (ClusterIP, LoadBalancer)
   - Ingress (SSL termination)
   - ConfigMaps and Secrets
   - Cross-reference with SUP-02

Phase 7D Work (15 hours):
5. Create C3-05: Identity Platform Components (2h)
6. Create C3-06: Cloud KMS Components (1.5h)
7. Create C3-07: Monitoring Components (2h)
8. Create SUP-02: GKE Deployment Architecture (2.5h)
9. Create SUP-03: VPC Network Architecture (2h)
10. Fix Integration Guides (3h):
    - coditect-application-integration.md
    - day-1-execution-guide.md
    - implementation-roadmap.md
11. Fix Project Planning Docs (2h):
    - cloud-kms-deployment-summary.md
    - cloud-kms-setup.md
    - CONTRIBUTING.md
    - cloud-agnostic-stack-analysis.md

Use Django patterns from:
- Phase 7A diagrams (C3-01, C4-01, SUP-01, C4-04, C3-02, C3-03)
- ADR-007: Django Multi-Tenant Architecture
- Existing Django code: models.py, views.py, serializers.py

Acceptance Criteria:
- All diagrams use Django patterns
- All code examples compile
- Cross-references validated
- Mermaid diagrams properly formatted
- Zero FastAPI references

Target: Complete Phase 7C-7D (23 hours over 5 days)
"""
)

Agent 11: Technical Writer (Days 6-7, 6.5 hours)

Task: Phase 7E Validation Priority: P1 Agent Type: codi-documentation-writer

Invocation:

Task(
    subagent_type="codi-documentation-writer",
    description="Complete Phase 7E validation and reporting",
    prompt="""Complete Phase 7E validation tasks:

1. Validate Django Code Compiles (2h):
   - Extract all Django code examples from documentation
   - Create test Django project
   - Verify all models compile without errors
   - Verify all serializers compile without errors
   - Verify all views compile without errors
   - Test database migrations
   - Test API endpoint functionality

2. Create Cross-Reference Index (1.5h):
   - Map all sequence diagrams to ADRs
   - Map all ADRs to C3/C4 diagrams
   - Map all workflow docs to sequence diagrams
   - Verify all cross-references are valid
   - Generate cross-reference matrix
   - Identify broken links

3. Comprehensive Gap Analysis (2h):
   - Identify missing documentation
   - Identify inconsistencies across documents
   - Identify outdated documentation
   - Categorize gaps by priority (P0/P1/P2)
   - Create remediation plan with time estimates
   - Generate gap analysis report

4. Update PROJECT-PLAN & TASKLIST (1h):
   - Mark all Phase 7 tasks complete
   - Update progress percentages in TASKLIST
   - Update timeline estimates in PROJECT-PLAN
   - Update completion statistics
   - Commit all documentation changes

Acceptance Criteria:
- All Django code examples compile successfully
- Cross-reference index complete and verified
- Gap analysis document created
- project-plan.md and tasklist.md updated
- Phase 7 100% complete

Target: Phase 7 completion with full validation
"""
)

5. Task Dependencies & Critical Path

Dependency Graph

Day 1:
Redis Deployment (30min) ────┐
                             ├──► Services Layer (2-3h) ────┐
                             │                              ├──► Test Suite Start (1-2h)
                             └──────────────────────────────┘

Day 2:
Test Suite Complete (3-4h) ───┐
                              ├──► Dockerfile (1h) ──┐
                              │                      ├──► K8s Manifests (2h)
                              └──────────────────────┘

Day 3:
Build + Push Image (30min) ───► Deploy to GKE (2h) ───┐
                                                       ├──► SSL + DNS (2h) ───► E2E Tests (2h)
                                                       └────────────────────────────────────────►

Parallel (Days 1-7):
Phase 7C-7D Diagrams (Days 1-5, 23h) ───► Phase 7E Validation (Days 6-7, 6.5h)

Critical Path Timeline

Backend MVP (CRITICAL PATH - 2-3 days):

T+0h:    Redis deployment starts
T+0.5h:  Redis operational, services layer starts
T+3.5h:  Services layer complete, tests start
T+5h:    Day 1 complete (Services + Test Start)

T+24h:   Day 2 starts
T+27h:   Tests complete, Dockerfile starts
T+28h:   Dockerfile complete, K8s manifests start
T+30h:   Day 2 complete (Tests + Docker + K8s)

T+48h:   Day 3 starts
T+50h:   Deployed to GKE
T+52h:   SSL + DNS configured
T+54h:   E2E tests complete
T+54h:   Backend MVP COMPLETE ✅

Phase 7 Documentation (PARALLEL - 6-7 days):

Days 1-5:  Phase 7C-7D diagrams (23 hours at 4-5 hours/day)
Days 6-7:  Phase 7E validation (6.5 hours at 3-4 hours/day)

6. Resource Requirements

Personnel

Backend Track (Days 1-3):

Backend Developer: 100% allocation (12-14 hours total)
- Services layer implementation
- Cloud KMS integration
- Code reviews
DevOps Engineer: 80% allocation (10-12 hours total)
- Redis deployment
- Dockerfile creation
- K8s manifests
- GKE deployment
- SSL + DNS configuration
QA Engineer: 60% allocation (8-10 hours total)
- Test suite creation
- End-to-end testing
- Performance validation

Documentation Track (Days 1-7):

Technical Writer: 60% allocation (30 hours total)
- Phase 7C-7D diagrams
- Phase 7E validation
- Gap analysis and reporting

Tools & Infrastructure

Required Tools:

OpenTofu 1.10.7 (infrastructure deployment)
kubectl (Kubernetes management)
Docker (container building)
gcloud CLI (GCP operations)
pytest (backend testing)
curl (API testing)

Infrastructure Costs:

Development environment: $310/month (with Redis)
No additional costs for MVP deployment

7. Risk Mitigation Strategies

High-Priority Risks

Risk	Probability	Impact	Mitigation
Redis deployment fails	Low	HIGH	Use verified OpenTofu module, test locally first
Cloud KMS integration issues	Medium	HIGH	KMS already deployed, use existing examples
Tests take longer than expected	Medium	MEDIUM	Prioritize critical path tests, defer edge cases
GKE deployment errors	Low	HIGH	Dry-run manifests, validate before apply
SSL certificate delays	Medium	MEDIUM	Use Let's Encrypt staging first, then production
Phase 7 delays MVP	ZERO	N/A	Runs in parallel, no blocking dependencies

Contingency Plans

If Redis deployment fails:

Fallback: Deploy manually via gcloud CLI
Estimated recovery: +1 hour

If tests take too long:

Strategy: Defer non-critical tests to post-MVP
Minimum viable: 60% coverage instead of 80%

If GKE deployment fails:

Rollback: Revert to previous working state
Debug: Check pod logs, events, describe deployment
Estimated recovery: +2 hours

If SSL takes too long:

Temporary: Use HTTP for initial testing
Deploy SSL in parallel (doesn't block API testing)

8. Success Criteria

Backend MVP Launch Criteria

Must-Have (P0):

Nice-to-Have (P1):

Test coverage ≥80%
Performance testing (100 concurrent requests)
Monitoring dashboards configured
Alerting configured (PagerDuty/Slack)

Phase 7 Completion Criteria

Must-Have (P0):

All C3 diagrams created (8 diagrams)
All C4 diagrams created (5 diagrams)
All supporting diagrams created (4 diagrams)
Sequence diagram 15 completed (15 total)
Zero FastAPI references in P0/P1 files

Nice-to-Have (P1):

All Django code examples compile without errors
Cross-reference index complete and verified
Comprehensive gap analysis document created
PROJECT-PLAN and TASKLIST updated

9. Timeline & Milestones

Week 1 (December 1-3, 2025)

Day 1 - Monday (Today):

Morning: Deploy Redis (30 min)
Morning: Create services layer (2-3 hours)
Afternoon: Start test suite (1-2 hours)
Milestone: Redis operational, services 50% complete

Day 2 - Tuesday:

Morning: Complete tests (3-4 hours)
Afternoon: Create Dockerfile (1 hour)
Afternoon: Create K8s manifests (2 hours)
Milestone: Tests complete, containerization ready

Day 3 - Wednesday:

Morning: Deploy to GKE (2-3 hours)
Afternoon: Configure SSL + DNS (2-3 hours)
Evening: End-to-end testing
Milestone: Backend MVP COMPLETE ✅

Week 2 (December 4-10, 2025)

Days 4-8 (Thursday-Monday):

Technical writer completes Phase 7C-7D diagrams
Milestone: All diagrams created

Days 9-10 (Tuesday-Wednesday):

Technical writer completes Phase 7E validation
Milestone: Phase 7 100% COMPLETE ✅

10. Monitoring & Progress Tracking

Daily Standup Questions

Backend Track:

What was completed yesterday?
What's planned for today?
Any blockers or risks?
On track for 2-3 day timeline?

Documentation Track:

How many diagrams completed?
Current completion percentage?
Any quality issues found?
On track for 6-7 day timeline?

Progress Metrics

Backend MVP:

Tasks completed / 12 total
Test coverage percentage
Deployment status (local → dev → prod)
Endpoint availability (3/3 working)

Phase 7 Documentation:

Diagrams completed / 15 total
FastAPI references remaining
Code compilation errors
Cross-reference validation status

Communication Channels

Slack Channels:

#backend-mvp - Backend development updates
#phase-7-docs - Documentation progress
#blockers - Critical issues requiring immediate attention

Status Updates:

Daily: Brief Slack update in respective channels
End of Day 1: Redis status, services progress
End of Day 2: Test results, Docker build status
End of Day 3: Deployment status, MVP launch confirmation

11. Post-MVP Actions

Immediate Follow-Up (Days 4-5)

After Backend MVP launches:

Monitor production for 24 hours
Fix any critical bugs discovered
Optimize performance (if needed)
Complete remaining Phase 7 documentation

Week 2 Priorities

Phase 7 Completion (6-7 days)
- All diagrams created
- All FastAPI→Django conversions complete
- Gap analysis finalized
Frontend Development (optional, can start Week 3)
- React 18 + TypeScript dashboard
- Integration with backend API
- Deployment to GKE
Production Hardening (Week 3-4)
- Security audit
- Performance optimization
- Backup and disaster recovery
- Compliance documentation

12. Conclusion

This plan provides a clear, actionable roadmap to:

Backend MVP in 2-3 days via focused multi-agent coordination
Phase 7 documentation complete in 6-7 days via parallel track
Zero blocking dependencies between tracks (maximum efficiency)

Key Success Factors:

Redis deployment is ONLY blocker (30 min fix)
Django backend 60% complete (huge head start)
Clear agent assignments with specific prompts
Parallel execution maximizes resource utilization
Comprehensive monitoring ensures early issue detection

Next Action: Deploy Redis Memorystore (30 min) to unblock entire critical path.

Plan Owner: CODITECT Infrastructure Team Last Updated: November 30, 2025 Next Review: December 1, 2025 (after Day 1 completion) Target Completion: December 3, 2025 (Backend MVP) + December 10, 2025 (Phase 7)

Executive Summary​

1. Current State Assessment​

Completed Work (47/120 tasks, 41%)​

Critical Blocker Discovered​

Missing Components (40% of Backend)​

2. Critical Path Validation​

Backend MVP Critical Path​

Phase 7 Deferred Work (NOT Blocking MVP)​

3. Resource Allocation Strategy​

Track 1: Backend MVP (Critical Path) - P0​

Track 2: Phase 7 Documentation (Parallel) - P1​

4. Multi-Agent Coordination Plan​

Phase 1: Immediate Actions (Day 1 - 4-5 hours)​

Phase 2: Complete Backend Implementation (Day 2 - 5-6 hours)​

Phase 3: Deployment & Validation (Day 3 - 4-6 hours)​

Phase 4: Phase 7 Documentation (Parallel Track - Days 1-7)​

5. Task Dependencies & Critical Path​

Dependency Graph​

Critical Path Timeline​

6. Resource Requirements​

Personnel​

Tools & Infrastructure​

7. Risk Mitigation Strategies​

High-Priority Risks​

Contingency Plans​

8. Success Criteria​

Backend MVP Launch Criteria​

Phase 7 Completion Criteria​

9. Timeline & Milestones​

Week 1 (December 1-3, 2025)​

Week 2 (December 4-10, 2025)​

10. Monitoring & Progress Tracking​

Daily Standup Questions​

Progress Metrics​

Communication Channels​

11. Post-MVP Actions​

Immediate Follow-Up (Days 4-5)​

Week 2 Priorities​

12. Conclusion​

Executive Summary

1. Current State Assessment

Completed Work (47/120 tasks, 41%)

Critical Blocker Discovered

Missing Components (40% of Backend)

2. Critical Path Validation

Backend MVP Critical Path

Phase 7 Deferred Work (NOT Blocking MVP)

3. Resource Allocation Strategy

Track 1: Backend MVP (Critical Path) - P0

Track 2: Phase 7 Documentation (Parallel) - P1

4. Multi-Agent Coordination Plan

Phase 1: Immediate Actions (Day 1 - 4-5 hours)

Phase 2: Complete Backend Implementation (Day 2 - 5-6 hours)

Phase 3: Deployment & Validation (Day 3 - 4-6 hours)

Phase 4: Phase 7 Documentation (Parallel Track - Days 1-7)

5. Task Dependencies & Critical Path

Dependency Graph

Critical Path Timeline

6. Resource Requirements

Personnel

Tools & Infrastructure

7. Risk Mitigation Strategies

High-Priority Risks

Contingency Plans

8. Success Criteria

Backend MVP Launch Criteria

Phase 7 Completion Criteria

9. Timeline & Milestones

Week 1 (December 1-3, 2025)

Week 2 (December 4-10, 2025)

10. Monitoring & Progress Tracking

Daily Standup Questions

Progress Metrics

Communication Channels

11. Post-MVP Actions

Immediate Follow-Up (Days 4-5)

Week 2 Priorities

12. Conclusion