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Autonomous .Claude Framework 8 Week Implementation Plan

Autonomous .Claude Framework - 8-Week Implementation Plan

From 78% to 100% Autonomous Operation​

Version: 1.0.0 Date Created: 2025-11-13 Status: Ready for Execution Target Completion: 8 weeks (January 2026) Total Effort: 320 hours (2 engineers)

Executive Summary​

This plan details the step-by-step implementation to transform the .Claude automation framework from 78% complete (human-in-the-loop orchestration) to 100% autonomous operation where agents can discover, communicate with, and coordinate tasks without human intervention.

Current State​

  • all agents, all commands, 189 skills cataloged
  • 7/9 orchestration modules working
  • CRITICAL GAP: No inter-agent communication
  • Missing: Message Bus, Task Queue, Circuit Breaker, Testing, Monitoring

Target State​

  • 100% autonomous operation
  • Full error resilience with circuit breakers
  • Complete observability (metrics, traces, logs, dashboards)
  • 80%+ test coverage with CI/CD pipeline
  • Production-ready deployment automation

Success Metrics​

MetricCurrentTargetMeasurement
Autonomy0% (human-in-loop)95%% tasks completed without human
LatencyN/A<5sTime from enqueue to agent start
Throughput1 task/min100 tasks/minTasks completed per minute
ReliabilityN/A99.9% uptime% time system available
Recovery TimeN/A<60sTime to recover from failure
Agent UtilizationN/A70% avg% time agents busy

Resource Requirements​

Team​

  • 2x Full-Stack Engineers (Python, async/await, distributed systems)
  • 1x DevOps Engineer (part-time, weeks 1-2 and 7-8)

Infrastructure​

  • RabbitMQ cluster (3 nodes, HA)
  • Redis cluster (3 nodes, HA)
  • PostgreSQL 15+ (existing)
  • S3 bucket (state backups)
  • Prometheus + Grafana + Jaeger stack
  • Staging environment (mirrors production)

Budget​

  • Infrastructure: $500/month (8 weeks = $1,000)
  • Monitoring tools: $200/month (8 weeks = $400)
  • Total: ~$1,400

Timeline Overview (Gantt Chart)​

Week 1-2: Phase 1 - Foundation (P0 - CRITICAL)
├─ Week 1
│  ├─ Day 1-2: Infrastructure setup
│  ├─ Day 3-5: Agent Discovery Service
│  └─ Day 5: Unit tests
├─ Week 2
│  ├─ Day 1-4: Message Bus implementation
│  ├─ Day 3-5: Task Queue Manager
│  └─ Day 5: Integration tests
└─ Milestone 1: Agents can discover and communicate

Week 3-4: Phase 2 - Resilience (P0 - CRITICAL)
├─ Week 3
│  ├─ Day 1-2: Circuit Breaker Service
│  ├─ Day 3-4: Retry Policy Engine
│  └─ Day 5: Integration tests
├─ Week 4
│  ├─ Day 1-4: Distributed State Manager
│  └─ Day 5: Stress tests
└─ Milestone 2: System handles failures gracefully

Week 5-6: Phase 3 - Observability (P1 - HIGH)
├─ Week 5
│  ├─ Day 1-3: Metrics Collection (Prometheus)
│  ├─ Day 4-5: Distributed Tracing (Jaeger)
│  └─ Day 5: Dashboard setup
├─ Week 6
│  ├─ Day 1-2: Structured Logging
│  ├─ Day 3-4: Grafana Dashboards
│  └─ Day 5: Alert configuration
└─ Milestone 3: Full observability operational

Week 7-8: Phase 4 - Polish (P1/P2 - MEDIUM)
├─ Week 7
│  ├─ Day 1-3: CLI Integration
│  ├─ Day 4-5: API Documentation
│  └─ Day 5: Deployment automation
├─ Week 8
│  ├─ Day 1-2: Load testing (100+ tasks)
│  ├─ Day 3-4: Performance tuning
│  └─ Day 5: Production deployment
└─ Milestone 4: Production-ready system

Phase 1: Foundation (Weeks 1-2)​

Goal: Core infrastructure for autonomous operation Priority: P0 (CRITICAL - Blocker) Duration: 10 days Effort: 80 hours

Success Criteria​

  • Agents can discover each other by capability
  • Agents can send/receive tasks via message bus
  • Tasks enqueued with priority and dependencies
  • First autonomous workflow: orchestrator → agent A → agent B → result
  • 80%+ unit test coverage for new components

1.1 Infrastructure Setup (2 days, 16 hours)​

Priority: P0 Dependencies: None Owner: DevOps Engineer

Tasks​

  • Task 1.1.1: Install and configure RabbitMQ cluster (4 hours)

    • Sub-task: Deploy RabbitMQ 3.12+ on 3 nodes (HA configuration)
    • Sub-task: Configure virtual host /claude-agents
    • Sub-task: Create admin user with proper permissions
    • Sub-task: Enable management plugin for monitoring
    • Sub-task: Configure persistent message storage
    • Sub-task: Test cluster failover (kill 1 node, verify recovery)
    • Acceptance: RabbitMQ cluster operational, management UI accessible at http://localhost:15672

  • Task 1.1.2: Install and configure Redis cluster (4 hours)

    • Sub-task: Deploy Redis 7+ on 3 nodes (master-replica setup)
    • Sub-task: Enable persistence (AOF + RDB)
    • Sub-task: Configure maxmemory policy (allkeys-lru)
    • Sub-task: Set up Redis Sentinel for auto-failover
    • Sub-task: Test failover (kill master, verify replica promotion)
    • Acceptance: Redis cluster operational, can read/write keys, auto-failover works

  • Task 1.1.3: Set up Python development environment (2 hours)

    • Sub-task: Create virtual environment (Python 3.10+)
    • Sub-task: Install dependencies (aio_pika, Redis-py, pybreaker, prometheus_client)
    • Sub-task: Install dev dependencies (pytest, pytest-asyncio, pytest-cov, black, mypy)
    • Sub-task: Configure pre-commit hooks (black, mypy, tests)
    • Acceptance: pip install -r requirements.txt succeeds, pre-commit hooks work

  • Task 1.1.4: Create project structure (2 hours)

    • Sub-task: Create directory structure:


      .claude/
      ├── orchestration/
      │   ├── __init__.py
      │   ├── agent_discovery.py
      │   ├── message_bus.py
      │   ├── task_queue.py
      │   ├── circuit_breaker.py
      │   ├── state_manager.py
      │   └── monitoring.py
      ├── tests/
      │   ├── unit/
      │   ├── integration/
      │   └── e2e/
      ├── config/
      │   ├── rabbitmq.yaml
      │   ├── redis.yaml
      │   └── prometheus.yaml
      └── docker/
          ├── docker-compose.yml
          └── Dockerfile

    • Sub-task: Create requirements.txt with all dependencies

    • Sub-task: Create pyproject.toml for build configuration

    • Sub-task: Create .env.example with configuration template

    • Acceptance: Directory structure matches design, empty Python files importable

  • Task 1.1.5: Set up local Docker Compose (4 hours)

    • Sub-task: Create docker-compose.yml with RabbitMQ, Redis, PostgreSQL
    • Sub-task: Add health checks for all services
    • Sub-task: Configure persistent volumes
    • Sub-task: Create make start, make stop, make logs commands
    • Sub-task: Write README for local development setup
    • Acceptance: docker-compose up -d starts all services, health checks pass

Testing​

  • All services start successfully
  • Can connect to RabbitMQ and send/receive messages
  • Can read/write to Redis
  • Failover tests pass for both RabbitMQ and Redis

1.2 Agent Discovery Service (3 days, 24 hours)​

Priority: P0 Dependencies: 1.1 (Infrastructure Setup) Owner: Engineer 1

Tasks​

  • Task 1.2.1: Design Redis schema for agent registry (2 hours)

    • Sub-task: Define data structures for Agent, AgentCapability, AgentStatus
    • Sub-task: Design Redis keys:
      • agent:{agent_id} - Hash for agent metadata
      • capability:{capability_name} - Set of agent_ids
      • agent_status:{status} - Set of agent_ids (AVAILABLE, BUSY, etc.)
    • Sub-task: Define TTL strategy (5 minutes for heartbeat)
    • Sub-task: Create schema documentation in docs/redis-schema.md
    • Acceptance: Schema documented, peer-reviewed, handles 1000+ agents

  • Task 1.2.2: Implement AgentDiscoveryService class (8 hours)

    • Sub-task: Create agent_discovery.py with class skeleton
    • Sub-task: Implement register_agent() method
      • Store agent metadata in Redis hash
      • Add to capability indexes
      • Set TTL for auto-cleanup
    • Sub-task: Implement find_agents_by_capability() method
      • Query capability index
      • Filter by health score and load
      • Sort by least loaded
    • Sub-task: Implement get_agent() method (fetch by ID)
    • Sub-task: Implement heartbeat() method
      • Update status and load
      • Refresh TTL
    • Sub-task: Implement _is_available() helper method
      • Check status, health score, load ratio
    • Sub-task: Add type hints (mypy strict mode)
    • Sub-task: Add docstrings (Google style)
    • Acceptance: All methods implemented, type-checked, documented

  • Task 1.2.3: Add unit tests for AgentDiscoveryService (6 hours)

    • Sub-task: Test agent registration
      • Register agent, verify Redis keys created
      • Verify capability indexes updated
      • Verify TTL set correctly
    • Sub-task: Test capability matching
      • Register multiple agents with overlapping capabilities
      • Verify find_agents_by_capability returns correct agents
      • Verify sorting by load
    • Sub-task: Test health filtering
      • Register agents with different health scores
      • Verify filtering by min_health_score
    • Sub-task: Test load filtering
      • Register agents with different loads
      • Verify filtering by max_load_ratio
    • Sub-task: Test heartbeat and TTL refresh
      • Register agent, wait 4 minutes, heartbeat, verify not expired
      • Register agent, wait 6 minutes, verify expired (auto-cleanup)
    • Sub-task: Test error handling (Redis connection failure)
    • Acceptance: 80%+ code coverage, all tests pass

  • Task 1.2.4: Create usage examples and documentation (4 hours)

    • Sub-task: Create examples/agent_discovery_example.py
      • Show agent registration
      • Show capability search
      • Show heartbeat loop
    • Sub-task: Update docs/AGENT-DISCOVERY.md with:
      • Architecture overview
      • API reference
      • Usage examples
      • Performance characteristics (handles 10K agents)
    • Sub-task: Add inline code examples in docstrings
    • Acceptance: Examples run successfully, docs clear and complete

  • Task 1.2.5: Integration with existing orchestrator.py (4 hours)

    • Sub-task: Update orchestrator.py to use AgentDiscoveryService
    • Sub-task: Replace hardcoded agent list with dynamic discovery
    • Sub-task: Add agent registration on startup for all all agents
    • Sub-task: Add heartbeat loop (every 60 seconds)
    • Sub-task: Test agent discovery in real orchestration workflow
    • Acceptance: Orchestrator finds agents dynamically, no hardcoded list

Testing​

  • Unit tests: 80%+ coverage, all pass
  • Integration test: Register 100 agents, search by capability, verify performance
  • Stress test: 1000 agents, verify queries <100ms

1.3 Message Bus Implementation (4 days, 32 hours)​

Priority: P0 Dependencies: 1.1 (Infrastructure Setup) Owner: Engineer 2

Tasks​

  • Task 1.3.1: Design RabbitMQ exchange and queue topology (4 hours)

    • Sub-task: Define exchanges:
      • agent.tasks (topic) - Route tasks to specific agents
      • agent.broadcasts (fanout) - Broadcast to all agents
      • agent.responses (direct) - Point-to-point responses
    • Sub-task: Define queue naming convention:
      • agent.{agent_id}.tasks - One per agent
      • agent.{agent_id}.responses - One per agent
      • orchestrator.priority - High-priority tasks
    • Sub-task: Define routing keys:
      • agent.{agent_id} for tasks
      • {agent_id}.response for responses
    • Sub-task: Configure priority levels (0-10)
    • Sub-task: Document topology in docs/message-bus-topology.md
    • Acceptance: Topology documented, supports 100+ agents, handles 1000 msg/sec

  • Task 1.3.2: Implement AgentMessage data class (2 hours)

    • Sub-task: Create message_bus.py with AgentMessage dataclass
    • Sub-task: Add fields: id, from_agent, to_agent, task_id, message_type, payload, correlation_id, timestamp, reply_to
    • Sub-task: Add serialization methods (to_dict, from_dict)
    • Sub-task: Add validation (non-empty fields, valid UUIDs)
    • Sub-task: Add type hints and docstrings
    • Acceptance: AgentMessage serializable to JSON, validated

  • Task 1.3.3: Implement MessageBus class (12 hours)

    • Sub-task: Create MessageBus class skeleton
    • Sub-task: Implement connect() method
      • Establish robust connection to RabbitMQ
      • Declare all exchanges
      • Handle connection errors with retry
    • Sub-task: Implement send_task() method
      • Create AgentMessage
      • Publish to task exchange with routing key
      • Set priority and delivery mode (persistent)
      • Return correlation_id for tracking
    • Sub-task: Implement send_response() method
      • Create response message
      • Publish to response exchange
      • Use correlation_id from original message
    • Sub-task: Implement broadcast_event() method
      • Publish to fanout exchange
      • All agents receive event
    • Sub-task: Implement subscribe() method
      • Declare agent-specific queue
      • Bind to task and broadcast exchanges
      • Start consuming with callback
      • Handle message acknowledgment
    • Sub-task: Implement wait_for_response() method
      • Wait for message with specific correlation_id
      • Timeout after specified seconds
      • Return response or raise TimeoutError
    • Sub-task: Add connection recovery (reconnect on failure)
    • Sub-task: Add graceful shutdown (close connections)
    • Acceptance: All methods implemented, connection resilient, graceful shutdown

  • Task 1.3.4: Add unit tests for MessageBus (8 hours)

    • Sub-task: Test connection and exchange creation
    • Sub-task: Test send_task (verify message published)
    • Sub-task: Test send_response (verify correlation_id matches)
    • Sub-task: Test broadcast_event (verify all agents receive)
    • Sub-task: Test subscribe (verify callback invoked)
    • Sub-task: Test wait_for_response (verify timeout)
    • Sub-task: Test connection recovery (simulate RabbitMQ restart)
    • Sub-task: Test message priority (high priority processed first)
    • Sub-task: Mock RabbitMQ with testcontainers or fakeredis
    • Acceptance: 80%+ coverage, all tests pass, no flaky tests

  • Task 1.3.5: Create end-to-end message flow example (4 hours)

    • Sub-task: Create examples/message_bus_example.py
      • Agent A sends task to Agent B
      • Agent B processes and sends response
      • Agent A receives response
    • Sub-task: Create examples/broadcast_example.py
      • One agent broadcasts event
      • Multiple agents receive and react
    • Sub-task: Update docs/MESSAGE-BUS.md with:
      • Architecture diagram
      • API reference
      • Usage examples
      • Performance characteristics
    • Acceptance: Examples run successfully, docs complete

  • Task 1.3.6: Integration with orchestrator.py (2 hours)

    • Sub-task: Update orchestrator.py to use MessageBus
    • Sub-task: Replace direct agent calls with send_task()
    • Sub-task: Add subscribe loop for receiving responses
    • Sub-task: Test orchestrator → agent → orchestrator flow
    • Acceptance: Orchestrator communicates via message bus, no direct calls

Testing​

  • Unit tests: 80%+ coverage, all pass
  • Integration test: 10 agents sending tasks to each other
  • Performance test: 1000 messages/sec sustained throughput
  • Reliability test: RabbitMQ restart, verify message delivery resumes

1.4 Task Queue Manager (3 days, 24 hours)​

Priority: P0 Dependencies: 1.1 (Infrastructure Setup) Owner: Engineer 1

Tasks​

  • Task 1.4.1: Design task queue data structures (3 hours)

    • Sub-task: Define TaskPriority enum (CRITICAL=10, HIGH=7, MEDIUM=5, LOW=3, BACKGROUND=1)
    • Sub-task: Define Task dataclass (id, description, agent, status, priority, dependencies, created_at)
    • Sub-task: Design Redis data structures:
      • task:{task_id} - Hash for task metadata
      • task_queue:ready - Sorted set (priority queue)
      • task_queue:blocked - Set (tasks with unsatisfied dependencies)
      • task_queue:in_progress - Set (running tasks)
      • task:{task_id}:dependencies - Set (task IDs this depends on)
      • task:{task_id}:dependents - Set (task IDs depending on this)
    • Sub-task: Document schema in docs/task-queue-schema.md
    • Acceptance: Schema supports DAG dependencies, priority ordering, deadlock detection

  • Task 1.4.2: Implement TaskQueueManager class (10 hours)

    • Sub-task: Create task_queue.py with class skeleton
    • Sub-task: Implement enqueue() method
      • Store task in Redis
      • Store dependencies
      • Add to ready queue if no dependencies, else blocked queue
      • Return task_id
    • Sub-task: Implement dequeue() method
      • Pop highest priority task from ready queue
      • Mark as IN_PROGRESS
      • Return Task object
    • Sub-task: Implement complete() method
      • Update task status to COMPLETED
      • Remove from in_progress
      • Get dependent tasks
      • For each dependent:
        • Remove this task from dependencies
        • If no remaining dependencies, move to ready queue
    • Sub-task: Implement fail() method
      • Update task status to FAILED
      • Implement retry logic with exponential backoff
      • After max retries, mark as FAILED permanently
    • Sub-task: Implement detect_deadlocks() method
      • Build dependency graph from blocked tasks
      • Use DFS to find cycles
      • Return list of deadlocked task chains
    • Sub-task: Add type hints and docstrings
    • Acceptance: All methods implemented, handles dependencies correctly

  • Task 1.4.3: Add unit tests for TaskQueueManager (6 hours)

    • Sub-task: Test simple enqueue/dequeue
      • Enqueue task, dequeue, verify task returned
    • Sub-task: Test priority ordering
      • Enqueue HIGH, LOW, CRITICAL tasks
      • Dequeue, verify CRITICAL comes first
    • Sub-task: Test dependencies
      • Enqueue task A depends on B
      • Verify A in blocked queue
      • Complete B, verify A moves to ready queue
    • Sub-task: Test dependency chain (A→B→C)
      • Complete C, verify B moves to ready
      • Complete B, verify A moves to ready
    • Sub-task: Test retry logic
      • Fail task, verify retry with backoff
      • Fail 3 times, verify permanent failure
    • Sub-task: Test deadlock detection
      • Create circular dependency (A→B→C→A)
      • Verify detect_deadlocks() finds cycle
    • Acceptance: 80%+ coverage, all edge cases tested

  • Task 1.4.4: Implement deadlock resolution (3 hours)

    • Sub-task: Create resolve_deadlock() method
    • Sub-task: Strategy: Remove lowest priority task from cycle
    • Sub-task: Log deadlock resolution
    • Sub-task: Send notification (optional)
    • Sub-task: Test deadlock resolution
    • Acceptance: Deadlocks automatically resolved, system doesn't hang

  • Task 1.4.5: Create usage examples and documentation (2 hours)

    • Sub-task: Create examples/task_queue_example.py
      • Enqueue tasks with dependencies
      • Worker loop: dequeue → process → complete
      • Show priority handling
    • Sub-task: Update docs/TASK-QUEUE.md with:
      • Architecture diagram
      • API reference
      • Dependency examples
      • Deadlock detection
    • Acceptance: Examples run, docs complete

Testing​

  • Unit tests: 80%+ coverage, all pass
  • Integration test: 100 tasks with complex DAG dependencies
  • Performance test: 10,000 tasks enqueued, verify <1s dequeue latency
  • Stress test: Deadlock with 50 tasks in cycle, verify resolution

1.5 Phase 1 Integration and Testing (2 days, 16 hours)​

Priority: P0 Dependencies: 1.2, 1.3, 1.4 Owner: Both Engineers

Tasks​

  • Task 1.5.1: Create first autonomous workflow (6 hours)

    • Sub-task: Create workflow: Orchestrator → Agent A (code review) → Agent B (documentation) → Orchestrator
    • Sub-task: Agent A:
      • Registers with AgentDiscoveryService
      • Subscribes to MessageBus
      • Receives task, executes, publishes result
      • Sends sub-task to Agent B via MessageBus
    • Sub-task: Agent B:
      • Registers with AgentDiscoveryService
      • Subscribes to MessageBus
      • Receives task, executes, publishes result
    • Sub-task: Orchestrator:
      • Discovers Agent A via AgentDiscoveryService
      • Sends task via MessageBus
      • Waits for response
      • Logs result
    • Sub-task: Test end-to-end without human intervention
    • Acceptance: Workflow completes successfully, no human input required

  • Task 1.5.2: Write integration tests (6 hours)

    • Sub-task: Test agent discovery + message bus integration
      • Register agent, send task via message bus, verify delivery
    • Sub-task: Test message bus + task queue integration
      • Enqueue task, message bus delivers to agent, agent completes
    • Sub-task: Test full stack integration
      • Orchestrator → discovery → queue → message bus → agent → response
    • Sub-task: Test failure scenarios
      • Agent not found, task fails gracefully
      • Message bus down, retry works
      • Redis down, recover when it comes back
    • Acceptance: All integration tests pass, <5s end-to-end latency

  • Task 1.5.3: Performance benchmarking (2 hours)

    • Sub-task: Benchmark agent discovery (1000 agents, search time)
    • Sub-task: Benchmark message bus (1000 messages/sec throughput)
    • Sub-task: Benchmark task queue (10,000 tasks enqueued/dequeued)
    • Sub-task: Document results in docs/PERFORMANCE-BENCHMARKS.md
    • Acceptance: All benchmarks meet targets (see table in Executive Summary)

  • Task 1.5.4: Code review and refactoring (2 hours)

    • Sub-task: Peer review all new code (agent_discovery, message_bus, task_queue)
    • Sub-task: Refactor based on feedback
    • Sub-task: Ensure consistent code style (run black, mypy)
    • Sub-task: Update docstrings and type hints
    • Acceptance: All code passes review, mypy strict mode, 100% type coverage

Milestone 1 Completion Checklist​

  • Agents can discover each other by capability
  • Agents can send tasks via message bus
  • Tasks auto-queue with dependencies
  • First autonomous multi-agent workflow works end-to-end
  • Unit tests: 80%+ coverage, all pass
  • Integration tests: All pass
  • Performance benchmarks: Meet targets

Phase 2: Resilience (Weeks 3-4)​

Goal: Error handling and recovery Priority: P0 (CRITICAL) Duration: 10 days Effort: 80 hours

Success Criteria​

  • Circuit breakers prevent cascading failures
  • Tasks automatically retry with exponential backoff
  • State syncs across nodes (multi-user support)
  • System recovers from agent failures within 60 seconds
  • Zero data loss during failures

2.1 Circuit Breaker Service (2 days, 16 hours)​

Priority: P0 Dependencies: Phase 1 complete Owner: Engineer 1

Tasks​

  • Task 2.1.1: Install and configure PyBreaker library (1 hour)

    • Sub-task: Add pybreaker==1.0.0 to requirements.txt
    • Sub-task: Install library
    • Sub-task: Test basic circuit breaker functionality
    • Acceptance: PyBreaker installed, basic example works

  • Task 2.1.2: Implement AgentCircuitBreaker class (8 hours)

    • Sub-task: Create circuit_breaker.py with class skeleton
    • Sub-task: Implement get_breaker() method
      • Create circuit breaker per agent
      • Configure fail_max=5 (open after 5 failures)
      • Configure timeout_duration=60 (stay open for 60 seconds)
      • Exclude TimeoutError from failure count
    • Sub-task: Implement call_agent() method
      • Call agent function through circuit breaker
      • Catch CircuitBreakerError
      • Find fallback agent with same capability
      • Retry with fallback agent
    • Sub-task: Implement _find_fallback() method
      • Query AgentDiscoveryService for agents with same capabilities
      • Exclude failing agent
      • Return least loaded agent
    • Sub-task: Implement get_status() method
      • Return circuit breaker state (closed, open, half_open)
    • Sub-task: Add logging for state transitions
    • Sub-task: Add type hints and docstrings
    • Acceptance: All methods implemented, circuit breaker protects agents

  • Task 2.1.3: Add unit tests for AgentCircuitBreaker (4 hours)

    • Sub-task: Test circuit opens after 5 failures
    • Sub-task: Test circuit stays open for 60 seconds
    • Sub-task: Test half_open state (1 request allowed)
    • Sub-task: Test fallback agent selection
    • Sub-task: Test excluded exceptions (TimeoutError)
    • Sub-task: Test get_status() returns correct state
    • Acceptance: 80%+ coverage, all edge cases tested

  • Task 2.1.4: Integration with MessageBus and Orchestrator (3 hours)

    • Sub-task: Wrap MessageBus.send_task() with circuit breaker
    • Sub-task: Wrap agent execution with circuit breaker
    • Sub-task: Test cascading failure prevention
      • Agent A fails 5 times
      • Circuit opens
      • Future requests go to fallback agent
    • Acceptance: Circuit breaker prevents cascading failures

Testing​

  • Unit tests: 80%+ coverage, all pass
  • Integration test: Fail agent 5 times, verify circuit opens, verify fallback works
  • Stress test: 1000 requests to failing agent, verify no cascading failure

2.2 Retry Policy Engine (2 days, 16 hours)​

Priority: P0 Dependencies: 2.1 (Circuit Breaker) Owner: Engineer 2

Tasks​

  • Task 2.2.1: Design retry policy configuration (2 hours)

    • Sub-task: Define RetryPolicy dataclass
      • max_retries: int (default 3)
      • base_delay: float (default 2.0 seconds)
      • max_delay: float (default 60.0 seconds)
      • exponential_backoff: bool (default True)
      • jitter: bool (default True, adds randomness)
    • Sub-task: Define retryable vs non-retryable errors
      • Retryable: TimeoutError, ConnectionError, AgentBusyError
      • Non-retryable: ValidationError, AuthenticationError, NotFoundError
    • Sub-task: Document policy in docs/RETRY-POLICY.md
    • Acceptance: Policy documented, covers all error types

  • Task 2.2.2: Implement RetryPolicyEngine class (8 hours)

    • Sub-task: Create retry_engine.py with class skeleton
    • Sub-task: Implement calculate_delay() method
      • Calculate exponential backoff: delay = base_delay * (2 ** retry_count)
      • Cap at max_delay
      • Add jitter: delay * random(0.8, 1.2)
    • Sub-task: Implement should_retry() method
      • Check retry count < max_retries
      • Check error is retryable
      • Check circuit breaker not open
    • Sub-task: Implement execute_with_retry() async method
      • Try executing function
      • On failure, check should_retry()
      • Calculate delay, wait
      • Retry
      • Repeat until success or max_retries
    • Sub-task: Add retry metrics (count, delay, success/failure)
    • Sub-task: Add logging for each retry attempt
    • Sub-task: Add type hints and docstrings
    • Acceptance: Retry logic working with exponential backoff + jitter

  • Task 2.2.3: Add unit tests for RetryPolicyEngine (4 hours)

    • Sub-task: Test calculate_delay() with exponential backoff
      • Retry 0: 2s, Retry 1: 4s, Retry 2: 8s, etc.
    • Sub-task: Test max_delay cap (60s)
    • Sub-task: Test jitter adds randomness
    • Sub-task: Test should_retry() logic
      • Retryable error + retry count < max → True
      • Non-retryable error → False
      • Retry count >= max → False
    • Sub-task: Test execute_with_retry()
      • Success on first try → No retries
      • Fail 2 times, succeed on 3rd → 2 retries
      • Fail 4 times → Max retries exceeded
    • Acceptance: 80%+ coverage, all tests pass

  • Task 2.2.4: Integration with TaskQueue and MessageBus (2 hours)

    • Sub-task: Wrap TaskQueueManager.fail() with retry engine
    • Sub-task: Wrap MessageBus.send_task() with retry engine
    • Sub-task: Test retry in real workflow
      • Agent fails with TimeoutError
      • Task retries after 2s, 4s, 8s
      • Succeeds on 4th attempt
    • Acceptance: Tasks automatically retry on transient failures

Testing​

  • Unit tests: 80%+ coverage, all pass
  • Integration test: Simulate transient failure, verify retry succeeds
  • Performance test: 100 tasks with retries, verify total time reasonable

2.3 Distributed State Manager (4 days, 32 hours)​

Priority: P0 Dependencies: Phase 1 complete Owner: Both Engineers

Tasks​

  • Task 2.3.1: Set up AWS S3 bucket for state backups (2 hours)

    • Sub-task: Create S3 bucket claude-agent-states
    • Sub-task: Configure bucket lifecycle policy (delete after 90 days)
    • Sub-task: Enable versioning for rollback
    • Sub-task: Set up IAM role with s3:PutObject, s3:GetObject permissions
    • Sub-task: Test upload/download with boto3
    • Acceptance: S3 bucket operational, can upload/download state files

  • Task 2.3.2: Implement distributed locking with Redis (6 hours)

    • Sub-task: Implement acquire_lock() method
      • Use Redis SETNX with expiration
      • Generate unique lock ID
      • Store lock_id locally for release
    • Sub-task: Implement release_lock() method
      • Only release if we own the lock (check lock_id)
      • Use Lua script for atomic check-and-delete
    • Sub-task: Implement lock timeout and auto-extension
      • Extend lock TTL if operation still running
      • Use watchdog thread to auto-extend
    • Sub-task: Add deadlock detection (lock held > 5 minutes → force release)
    • Sub-task: Add type hints and docstrings
    • Acceptance: Distributed locks prevent concurrent state modifications

  • Task 2.3.3: Implement DistributedStateManager class (12 hours)

    • Sub-task: Extend existing StateManager class
    • Sub-task: Implement sync_from_cloud() method
      • Get S3 object metadata (last modified, ETag)
      • Compare with local file timestamp
      • Download if S3 is newer
      • Upload if local is newer
    • Sub-task: Implement sync_to_cloud() method
      • Upload local state to S3
      • Add metadata (project_id, version, updated_at)
    • Sub-task: Implement save_state() override
      • Acquire distributed lock
      • Sync from cloud (get latest)
      • Merge changes
      • Save locally
      • Sync to cloud
      • Release lock
    • Sub-task: Implement load_state() override
      • Sync from cloud first
      • Load into memory
    • Sub-task: Add conflict resolution (last-write-wins or custom merge)
    • Sub-task: Add retry logic for S3 operations
    • Sub-task: Add type hints and docstrings
    • Acceptance: State syncs across nodes, no data loss, conflicts resolved

  • Task 2.3.4: Add unit tests for DistributedStateManager (8 hours)

    • Sub-task: Test sync_from_cloud()
      • S3 newer than local → Download
      • Local newer than S3 → Upload
    • Sub-task: Test sync_to_cloud()
      • Upload state with metadata
    • Sub-task: Test distributed lock
      • Two nodes try to acquire lock
      • Only one succeeds
      • Second waits until first releases
    • Sub-task: Test concurrent state modifications
      • Two nodes save state concurrently
      • Verify no data loss
      • Verify conflict resolution
    • Sub-task: Test S3 failure handling
      • S3 down → Retry with backoff
      • S3 permanent failure → Fallback to local only
    • Sub-task: Mock S3 with moto library
    • Acceptance: 80%+ coverage, all concurrency tests pass

  • Task 2.3.5: Integration with orchestrator.py (4 hours)

    • Sub-task: Replace StateManager with DistributedStateManager
    • Sub-task: Add sync_from_cloud() on startup
    • Sub-task: Add sync_to_cloud() on save
    • Sub-task: Test multi-node scenario
      • Start orchestrator on Node 1
      • Save state
      • Start orchestrator on Node 2
      • Verify state synced
    • Acceptance: Multi-node orchestration works, state synced

Testing​

  • Unit tests: 80%+ coverage, all pass
  • Integration test: 3 nodes concurrently modifying state, verify consistency
  • Performance test: 1000 state saves, verify <1s latency per save
  • Reliability test: Kill S3 mid-save, verify retry succeeds

2.4 Phase 2 Integration and Testing (2 days, 16 hours)​

Priority: P0 Dependencies: 2.1, 2.2, 2.3 Owner: Both Engineers

Tasks​

  • Task 2.4.1: Create resilience test scenarios (8 hours)

    • Sub-task: Scenario 1 - Agent failure
      • Agent crashes mid-execution
      • Circuit breaker detects failure
      • Task retries with fallback agent
      • Verify task completes successfully
    • Sub-task: Scenario 2 - RabbitMQ failure
      • Kill RabbitMQ mid-workflow
      • Message bus reconnects
      • Tasks resume from queue
      • Verify no message loss
    • Sub-task: Scenario 3 - Redis failure
      • Kill Redis mid-save
      • Distributed lock released
      • State recovers from S3
      • Verify no data loss
    • Sub-task: Scenario 4 - Network partition
      • Simulate network partition between nodes
      • Verify distributed locks prevent split-brain
      • Verify state consistency after partition heals
    • Sub-task: Scenario 5 - Cascading failures
      • Multiple agents fail simultaneously
      • Circuit breakers open
      • System degrades gracefully
      • Verify no complete system failure
    • Acceptance: All resilience tests pass, recovery time <60s

  • Task 2.4.2: Write chaos engineering tests (4 hours)

    • Sub-task: Install chaos testing library (pumba, toxiproxy, or chaos-toolkit)
    • Sub-task: Create chaos scenarios
      • Random agent kills
      • Random network delays (100ms-1s)
      • Random service restarts
    • Sub-task: Run chaos tests for 1 hour
    • Sub-task: Verify system self-heals
    • Acceptance: System withstands 1 hour of chaos, maintains 99%+ uptime

  • Task 2.4.3: Performance tuning (2 hours)

    • Sub-task: Profile circuit breaker overhead (<1ms per call)
    • Sub-task: Tune retry delays (balance speed vs load)
    • Sub-task: Optimize S3 sync frequency (batch writes)
    • Sub-task: Document tuning parameters in docs/PERFORMANCE-TUNING.md
    • Acceptance: No performance regression from Phase 1

  • Task 2.4.4: Code review and documentation (2 hours)

    • Sub-task: Peer review all Phase 2 code
    • Sub-task: Update architecture diagrams with resilience components
    • Sub-task: Update docs/architecture.md
    • Sub-task: Create runbook for failure recovery in docs/RUNBOOK.md
    • Acceptance: All code reviewed, docs updated, runbook tested

Milestone 2 Completion Checklist​

  • Circuit breakers prevent cascading failures
  • Tasks automatically retry (3 attempts with exponential backoff)
  • State syncs across nodes via S3
  • System recovers from failures within 60 seconds
  • Chaos tests pass (1 hour of random failures)
  • Zero data loss demonstrated

Phase 3: Observability (Weeks 5-6)​

Goal: Visibility into system behavior Priority: P1 (HIGH) Duration: 10 days Effort: 80 hours

Success Criteria​

  • Real-time metrics visible in Prometheus
  • End-to-end distributed tracing in Jaeger
  • Structured JSON logs in Loki
  • Grafana dashboards operational
  • Alerts configured for critical events

3.1 Metrics Collection (Prometheus) (3 days, 24 hours)​

Priority: P1 Dependencies: Phase 2 complete Owner: Engineer 1

Tasks​

  • Task 3.1.1: Install and configure Prometheus (4 hours)

    • Sub-task: Deploy Prometheus server
    • Sub-task: Configure scrape targets (orchestrator, agents)
    • Sub-task: Configure retention (15 days)
    • Sub-task: Test Prometheus UI (http://localhost:9090)
    • Acceptance: Prometheus operational, scraping metrics

  • Task 3.1.2: Implement SystemMonitor class (12 hours)

    • Sub-task: Create monitoring.py with class skeleton
    • Sub-task: Define Prometheus metrics:
      • tasks_total (Counter) - Total tasks processed (labels: agent, status)
      • task_duration_seconds (Histogram) - Task execution duration (labels: agent)
      • agent_utilization (Gauge) - Agent load / max capacity (labels: agent)
      • circuit_breaker_state (Gauge) - CB state (labels: agent)
      • message_bus_messages_total (Counter) - Messages sent (labels: message_type)
      • task_queue_size (Gauge) - Queue size (labels: queue_name)
      • retry_attempts_total (Counter) - Retry attempts (labels: agent, error_type)
    • Sub-task: Implement metric recording methods:
      • record_task_start(task)
      • record_task_complete(task, duration, status)
      • record_agent_utilization(agent_id, utilization)
      • record_circuit_breaker_state(agent_id, state)
      • record_message_sent(message_type)
      • record_retry_attempt(agent_id, error_type)
    • Sub-task: Start Prometheus HTTP server on port 8000
    • Sub-task: Add type hints and docstrings
    • Acceptance: All metrics defined, recording methods working

  • Task 3.1.3: Instrument existing code with metrics (6 hours)

    • Sub-task: Add metrics to TaskQueueManager
      • Record enqueue, dequeue, complete, fail events
      • Track queue sizes
    • Sub-task: Add metrics to MessageBus
      • Record messages sent, received
      • Track message latency
    • Sub-task: Add metrics to AgentCircuitBreaker
      • Record circuit state transitions
    • Sub-task: Add metrics to RetryPolicyEngine
      • Record retry attempts
    • Sub-task: Test metrics endpoint (curl http://localhost:8000/metrics)
    • Acceptance: All critical paths instrumented, metrics visible

  • Task 3.1.4: Create Prometheus queries and alerts (2 hours)

    • Sub-task: Create queries:
      • Task success rate: rate(tasks_total{status="success"}[5m])
      • Average task duration: histogram_quantile(0.95, task_duration_seconds)
      • Agent utilization: agent_utilization
      • Circuit breaker open: circuit_breaker_state == 1
    • Sub-task: Create alerts:
      • High error rate: rate(tasks_total{status="failed"}[5m]) > 0.1
      • Circuit breaker open: circuit_breaker_state == 1
      • Queue backlog: task_queue_size > 1000
    • Sub-task: Configure Alertmanager for email/Slack notifications
    • Acceptance: Alerts trigger correctly

Testing​

  • Metrics endpoint returns valid Prometheus format
  • All metrics update in real-time
  • Queries return expected values
  • Alerts trigger when thresholds exceeded

3.2 Distributed Tracing (Jaeger + OpenTelemetry) (3 days, 24 hours)​

Priority: P1 Dependencies: 3.1 (Metrics) Owner: Engineer 2

Tasks​

  • Task 3.2.1: Install and configure Jaeger (3 hours)

    • Sub-task: Deploy Jaeger all-in-one
    • Sub-task: Configure storage backend (Elasticsearch or memory)
    • Sub-task: Test Jaeger UI (http://localhost:16686)
    • Acceptance: Jaeger operational, UI accessible

  • Task 3.2.2: Integrate OpenTelemetry SDK (8 hours)

    • Sub-task: Install opentelemetry-api, opentelemetry-sdk, opentelemetry-exporter-jaeger
    • Sub-task: Configure TracerProvider with Jaeger exporter
    • Sub-task: Create root tracer in SystemMonitor
    • Sub-task: Implement trace context propagation
      • Add trace_id and span_id to AgentMessage
      • Extract context from incoming messages
      • Inject context into outgoing messages
    • Sub-task: Test trace propagation (send task through 3 agents, verify single trace)
    • Acceptance: OpenTelemetry integrated, traces export to Jaeger

  • Task 3.2.3: Instrument code with spans (8 hours)

    • Sub-task: Add spans to TaskQueueManager
      • task_enqueue span
      • task_dequeue span
      • task_execution span (parent span for entire task)
    • Sub-task: Add spans to MessageBus
      • message_send span
      • message_receive span
    • Sub-task: Add spans to agent execution
      • agent_execute span (nested under task_execution)
      • Add span attributes (agent_id, task_id, capabilities)
    • Sub-task: Add spans to circuit breaker
      • circuit_breaker_call span
      • Add span events (state transitions)
    • Sub-task: Test end-to-end trace (orchestrator → agent A → agent B)
    • Acceptance: Complete trace visible in Jaeger, showing all hops

  • Task 3.2.4: Create trace analysis queries (3 hours)

    • Sub-task: Query by service (e.g., show all agent A traces)
    • Sub-task: Query by operation (e.g., show all task_execution spans)
    • Sub-task: Query by duration (e.g., show spans >10s)
    • Sub-task: Query by error (e.g., show failed spans)
    • Sub-task: Create latency breakdown diagram (where is time spent?)
    • Acceptance: Can diagnose performance bottlenecks using traces

  • Task 3.2.5: Document tracing usage (2 hours)

    • Sub-task: Create docs/DISTRIBUTED-TRACING.md
    • Sub-task: Add examples of trace queries
    • Sub-task: Add troubleshooting guide
    • Acceptance: Docs complete, team can use tracing

Testing​

  • Traces appear in Jaeger for all operations
  • Trace context propagates across agents
  • Can trace single request end-to-end
  • Latency breakdown accurate

3.3 Structured Logging (2 days, 16 hours)​

Priority: P1 Dependencies: Phase 2 complete Owner: Engineer 1

Tasks​

  • Task 3.3.1: Install and configure Loki (3 hours)

    • Sub-task: Deploy Loki and Promtail
    • Sub-task: Configure log aggregation from all services
    • Sub-task: Test Loki query UI (via Grafana)
    • Acceptance: Loki operational, logs visible

  • Task 3.3.2: Implement structured logging (8 hours)

    • Sub-task: Install Python-json-logger library
    • Sub-task: Create custom logger class
      • Output JSON format
      • Add context fields (trace_id, span_id, agent_id, task_id)
      • Add timestamp, level, message
    • Sub-task: Replace all print() statements with logger calls
    • Sub-task: Add log levels consistently
      • DEBUG: Verbose diagnostics
      • INFO: Key events (task started, completed)
      • WARNING: Retries, degraded performance
      • ERROR: Failures, exceptions
      • CRITICAL: System-wide failures
    • Sub-task: Test log correlation with traces (same trace_id)
    • Acceptance: All logs in JSON format, correlated with traces

  • Task 3.3.3: Create log queries and dashboards (3 hours)

    • Sub-task: Query logs by level (e.g., show all ERROR logs)
    • Sub-task: Query logs by agent (e.g., show all agent A logs)
    • Sub-task: Query logs by trace_id (e.g., show logs for specific request)
    • Sub-task: Create log-based alerts (e.g., >10 errors/minute)
    • Acceptance: Can search logs efficiently, alerts work

  • Task 3.3.4: Document logging best practices (2 hours)

    • Sub-task: Create docs/LOGGING.md
    • Sub-task: Add logging guidelines (what to log, when, at what level)
    • Sub-task: Add examples of good log messages
    • Acceptance: Docs complete, team follows guidelines

Testing​

  • All logs in JSON format
  • Logs correlated with traces (trace_id matches)
  • Can query logs by level, agent, trace_id
  • Log-based alerts trigger correctly

3.4 Grafana Dashboards (2 days, 16 hours)​

Priority: P1 Dependencies: 3.1, 3.2, 3.3 Owner: Both Engineers

Tasks​

  • Task 3.4.1: Install and configure Grafana (2 hours)

    • Sub-task: Deploy Grafana
    • Sub-task: Add Prometheus data source
    • Sub-task: Add Loki data source
    • Sub-task: Add Jaeger data source
    • Sub-task: Test Grafana UI (http://localhost:3000)
    • Acceptance: Grafana operational, data sources connected

  • Task 3.4.2: Create System Overview dashboard (6 hours)

    • Sub-task: Add panels:
      • Total tasks processed (Counter graph)
      • Task success rate (Percentage graph)
      • Average task duration (Line graph)
      • Agent utilization (Heatmap)
      • Circuit breaker states (State timeline)
      • Queue sizes (Multi-line graph)
    • Sub-task: Add variables (agent filter, time range)
    • Sub-task: Add annotations (deployments, incidents)
    • Acceptance: Dashboard shows real-time system health

  • Task 3.4.3: Create Agent Performance dashboard (4 hours)

    • Sub-task: Add panels per agent:
      • Task throughput
      • Error rate
      • Average latency
      • Circuit breaker state
    • Sub-task: Add agent comparison (side-by-side)
    • Acceptance: Can compare agent performance

  • Task 3.4.4: Create Trace Analysis dashboard (2 hours)

    • Sub-task: Integrate Jaeger panel
    • Sub-task: Show recent traces
    • Sub-task: Show slowest traces
    • Sub-task: Show error traces
    • Acceptance: Can drill into traces from dashboard

  • Task 3.4.5: Configure alerting (2 hours)

    • Sub-task: Configure notification channels (email, Slack)
    • Sub-task: Create alert rules:
      • High error rate (>10%)
      • Slow tasks (>30s)
      • Circuit breaker open
      • Queue backlog (>1000)
    • Sub-task: Test alerts (trigger manually)
    • Acceptance: Alerts sent to Slack/email

Testing​

  • All dashboards load without errors
  • All panels show live data
  • Variables and filters work
  • Alerts trigger and notify correctly

3.5 Phase 3 Integration and Testing (0 days, 0 hours)​

Priority: P1 Note: Testing is integrated into each section above

Milestone 3 Completion Checklist​

  • Prometheus scraping metrics from all services
  • Jaeger showing end-to-end traces
  • Loki aggregating structured logs
  • Grafana dashboards operational
  • Alerts configured and tested
  • Can diagnose issues using observability stack

Phase 4: Polish (Weeks 7-8)​

Goal: Production readiness Priority: P1/P2 (MEDIUM) Duration: 10 days Effort: 80 hours

Success Criteria​

  • System handles 100+ concurrent tasks
  • All documentation updated
  • CI/CD pipeline complete
  • Load tests pass
  • Production deployment successful

4.1 CLI Integration (3 days, 24 hours)​

Priority: P1 Dependencies: Phase 3 complete Owner: Engineer 1

Tasks​

  • Task 4.1.1: Create CLI commands for orchestration (12 hours)

    • Sub-task: Add claude agent list command
      • List all registered agents
      • Show status, capabilities, load
    • Sub-task: Add claude agent info <agent-id> command
      • Show detailed agent info
      • Show recent tasks
    • Sub-task: Add claude task submit <task-json> command
      • Submit task to queue
      • Return task_id
    • Sub-task: Add claude task status <task-id> command
      • Show task status (PENDING, IN_PROGRESS, COMPLETED, FAILED)
      • Show result if completed
    • Sub-task: Add claude task list command
      • List all tasks (with filters: status, agent)
    • Sub-task: Add claude queue status command
      • Show queue sizes (ready, blocked, in_progress)
    • Sub-task: Add claude circuit-breaker status command
      • Show all circuit breaker states
    • Sub-task: Add claude metrics command
      • Show key metrics (task count, success rate, latency)
    • Sub-task: Add type hints and help text
    • Acceptance: All CLI commands working, help text clear

  • Task 4.1.2: Add unit tests for CLI (6 hours)

    • Sub-task: Test each CLI command
    • Sub-task: Test error handling (invalid inputs)
    • Sub-task: Test output formatting (JSON, table)
    • Acceptance: 80%+ coverage, all tests pass

  • Task 4.1.3: Create CLI usage documentation (4 hours)

    • Sub-task: Create docs/CLI-REFERENCE.md
    • Sub-task: Add examples for each command
    • Sub-task: Add troubleshooting section
    • Acceptance: Docs complete, easy to follow

  • Task 4.1.4: Integration with existing CLI (2 hours)

    • Sub-task: Add new commands to existing CLI
    • Sub-task: Test backward compatibility
    • Acceptance: New commands integrated, no regressions

Testing​

  • All CLI commands work
  • Output format correct
  • Error messages helpful
  • Integration tests pass

4.2 API Documentation (2 days, 16 hours)​

Priority: P1 Dependencies: Phase 3 complete Owner: Engineer 2

Tasks​

  • Task 4.2.1: Install and configure Sphinx (2 hours)

    • Sub-task: Install Sphinx and extensions
    • Sub-task: Generate initial docs structure
    • Sub-task: Configure auto-build on file change
    • Acceptance: Sphinx operational, docs build

  • Task 4.2.2: Generate API documentation (8 hours)

    • Sub-task: Add module documentation:
      • agent_discovery.py
      • message_bus.py
      • task_queue.py
      • circuit_breaker.py
      • retry_engine.py
      • state_manager.py
      • monitoring.py
    • Sub-task: Add class and method documentation
    • Sub-task: Add usage examples
    • Sub-task: Add architecture diagrams (generated from code)
    • Acceptance: Complete API reference generated

  • Task 4.2.3: Create user guides (4 hours)

    • Sub-task: Create "Getting Started" guide
    • Sub-task: Create "How to Create an Agent" guide
    • Sub-task: Create "How to Submit a Task" guide
    • Sub-task: Create "Troubleshooting" guide
    • Acceptance: Guides complete, easy to follow

  • Task 4.2.4: Publish documentation (2 hours)

    • Sub-task: Build HTML docs
    • Sub-task: Deploy to GitHub Pages or Read the Docs
    • Sub-task: Add link to README
    • Acceptance: Docs published, accessible via URL

Testing​

  • Docs build without errors
  • All links work
  • Examples run successfully
  • Docs render correctly on mobile

4.3 Deployment Automation (3 days, 24 hours)​

Priority: P1 Dependencies: Phase 3 complete Owner: DevOps Engineer + Engineer 1

Tasks​

  • Task 4.3.1: Create Docker images (6 hours)

    • Sub-task: Create Dockerfile for orchestrator
    • Sub-task: Create Dockerfile for agents
    • Sub-task: Optimize image size (multi-stage builds)
    • Sub-task: Add health check endpoints
    • Sub-task: Test images locally
    • Acceptance: Docker images build, run successfully

  • Task 4.3.2: Create Kubernetes manifests (8 hours)

    • Sub-task: Create Deployment for orchestrator
    • Sub-task: Create StatefulSet for RabbitMQ, Redis
    • Sub-task: Create Services for all components
    • Sub-task: Create ConfigMaps for configuration
    • Sub-task: Create Secrets for credentials
    • Sub-task: Configure resource limits (CPU, memory)
    • Sub-task: Configure liveness and readiness probes
    • Sub-task: Test on local K8s cluster (minikube or kind)
    • Acceptance: K8s manifests deploy successfully

  • Task 4.3.3: Create CI/CD pipeline (6 hours)

    • Sub-task: Create GitHub Actions workflow
    • Sub-task: Add steps:
      • Lint (black, mypy)
      • Test (pytest with coverage)
      • Build Docker images
      • Push to registry
      • Deploy to staging
      • Run smoke tests
      • (Manual approval for production)
      • Deploy to production
    • Sub-task: Configure secrets (Docker Hub, K8s cluster)
    • Sub-task: Test pipeline end-to-end
    • Acceptance: CI/CD pipeline working, deploys to staging

  • Task 4.3.4: Create deployment runbook (4 hours)

    • Sub-task: Document deployment process
    • Sub-task: Document rollback process
    • Sub-task: Document scaling process
    • Sub-task: Create troubleshooting guide
    • Acceptance: Runbook complete, tested

Testing​

  • Docker images run without errors
  • K8s deployments healthy
  • CI/CD pipeline deploys successfully
  • Rollback works

4.4 Load Testing (2 days, 16 hours)​

Priority: P2 Dependencies: 4.3 (Deployment) Owner: Both Engineers

Tasks​

  • Task 4.4.1: Install and configure load testing tool (2 hours)

    • Sub-task: Install Locust or k6
    • Sub-task: Create load test script
    • Acceptance: Load testing tool operational

  • Task 4.4.2: Create load test scenarios (6 hours)

    • Sub-task: Scenario 1 - Steady load
      • 10 tasks/sec for 10 minutes
      • Verify system stable
    • Sub-task: Scenario 2 - Spike load
      • Ramp up from 10 to 100 tasks/sec in 1 minute
      • Verify system scales
    • Sub-task: Scenario 3 - Stress test
      • 1000 concurrent tasks
      • Verify system doesn't crash
    • Sub-task: Scenario 4 - Soak test
      • 50 tasks/sec for 1 hour
      • Verify no memory leaks
    • Acceptance: All scenarios defined, scripts ready

  • Task 4.4.3: Run load tests and collect results (4 hours)

    • Sub-task: Run each scenario
    • Sub-task: Collect metrics (latency, throughput, error rate)
    • Sub-task: Identify bottlenecks
    • Sub-task: Document results in docs/LOAD-TEST-RESULTS.md
    • Acceptance: Load tests complete, results documented

  • Task 4.4.4: Performance tuning based on results (4 hours)

    • Sub-task: Tune RabbitMQ (prefetch count, connection pooling)
    • Sub-task: Tune Redis (connection pooling, pipeline batching)
    • Sub-task: Tune orchestrator (concurrency limits, queue sizes)
    • Sub-task: Re-run load tests, verify improvement
    • Acceptance: System meets performance targets (100 tasks/min)

Testing​

  • Load tests run without errors
  • System handles 100+ concurrent tasks
  • No performance degradation over time
  • Error rate <1%

4.5 Production Deployment and Go-Live (0 days, 0 hours)​

Priority: P1 Dependencies: 4.1, 4.2, 4.3, 4.4 Owner: Both Engineers + DevOps

Tasks​

  • Task 4.5.1: Pre-deployment checklist (2 hours)

    • Sub-task: Verify all tests pass (unit, integration, E2E, load)
    • Sub-task: Verify all documentation updated
    • Sub-task: Verify monitoring and alerts configured
    • Sub-task: Verify rollback plan tested
    • Sub-task: Verify team trained on new system
    • Acceptance: All checklist items complete

  • Task 4.5.2: Phased rollout (8 hours)

    • Sub-task: Phase 1 - Deploy to staging, run smoke tests (1 hour)
    • Sub-task: Phase 2 - Route 10% of production traffic to new system (1 hour)
    • Sub-task: Monitor for 24 hours, verify metrics (24 hours)
    • Sub-task: Phase 3 - Route 50% of production traffic (1 hour)
    • Sub-task: Monitor for 24 hours, verify metrics (24 hours)
    • Sub-task: Phase 4 - Route 100% of production traffic (1 hour)
    • Sub-task: Monitor for 48 hours, verify metrics (48 hours)
    • Acceptance: 100% traffic on new system, no errors

  • Task 4.5.3: Deprecate old orchestrator (2 hours)

    • Sub-task: Archive old orchestrator.py
    • Sub-task: Update all references to use new system
    • Sub-task: Remove old code after 30 days (if no issues)
    • Acceptance: Old system fully replaced

  • Task 4.5.4: Post-deployment review (2 hours)

    • Sub-task: Review metrics (success rate, latency, error rate)
    • Sub-task: Collect team feedback
    • Sub-task: Document lessons learned
    • Sub-task: Create backlog for future improvements
    • Acceptance: Review complete, lessons documented

Milestone 4 Completion Checklist​

  • CLI commands working
  • API documentation published
  • CI/CD pipeline operational
  • Load tests passing
  • Production deployment successful
  • 100% traffic on new system
  • Team trained on new system

Risk Mitigation​

Risk Registry​

RiskProbabilityImpactMitigationOwner
RabbitMQ performance bottleneckMediumHighLoad test early (Week 2), optimize or replace with Kafka if neededEngineer 2
Redis data loss during failoverLowHighConfigure AOF persistence, test failover extensively (Week 4)DevOps
Complex dependency deadlocksMediumMediumImplement deadlock detection (Week 1), alerting (Week 5)Engineer 1
State sync conflicts (multi-node)MediumMediumImplement robust conflict resolution (Week 4), test thoroughlyBoth Engineers
Circuit breakers too aggressiveMediumLowTune fail_max and timeout_duration (Week 3), monitor closely (Week 5)Engineer 1
Observability overheadLowMediumBenchmark overhead (Week 5), disable if >5% impactEngineer 1
Team not trained on new systemHighHighCreate comprehensive docs (Week 7), hands-on training sessionBoth Engineers
Production deployment failureMediumCriticalPhased rollout (10%/50%/100%), robust rollback planDevOps

Rollback Plan​

If production deployment fails:

  1. Immediate Rollback (<5 minutes):

    • Route 100% traffic to old system
    • Verify old system operational
    • Alert team

  2. Investigation (1-2 hours):

    • Review logs, metrics, traces
    • Identify root cause
    • Create fix or mitigation

  3. Re-deployment:

    • Fix issue in staging
    • Re-run all tests
    • Retry phased rollout

Success Metrics Tracking​

Weekly Metrics Report​

Create weekly report with these metrics:

MetricWeek 1Week 2Week 3Week 4Week 5Week 6Week 7Week 8Target
Autonomy %0%20%40%60%70%80%90%95%95%
Test Coverage %0%40%60%75%80%80%85%90%80%
Tasks Completed01050100500100050001000010000
Avg Latency (s)N/A1086543<3<5
Error Rate %N/A10%5%2%1%0.5%0.1%<0.1%<1%
Uptime %N/A90%95%98%99%99.5%99.9%99.9%99.9%

Final Acceptance Criteria​

Before marking project complete:

  • All 8 milestones achieved
  • Autonomy: 95%+ (95+ out of 100 tasks complete without human)
  • Latency: <5s task dispatch
  • Throughput: 100+ tasks/min sustained
  • Reliability: 99.9% uptime over 7 days
  • Recovery: <60s from agent failure
  • Test Coverage: 80%+ across all components
  • Load Test: Handles 1000 concurrent tasks
  • Documentation: 100% complete and published
  • Team Training: 100% of team trained

Appendix: Command Reference​

Development Commands​

# Setup
make setup                    # Install all dependencies
make start                    # Start all services (Docker Compose)
make stop                     # Stop all services

# Testing
make test                     # Run all tests
make test-unit                # Run unit tests only
make test-integration         # Run integration tests only
make test-e2e                 # Run end-to-end tests
make test-coverage            # Run tests with coverage report

# Code Quality
make lint                     # Run linters (black, mypy)
make format                   # Format code (black)
make type-check               # Type checking (mypy)

# Development
make dev                      # Run in development mode (hot reload)
make logs                     # Tail logs from all services
make shell                    # Open interactive shell

# Deployment
make build                    # Build Docker images
make deploy-staging           # Deploy to staging
make deploy-prod              # Deploy to production
make rollback                 # Rollback deployment

# Monitoring
make metrics                  # Open Prometheus
make traces                   # Open Jaeger
make logs-ui                  # Open Loki/Grafana
make dashboards               # Open Grafana

Appendix: File Manifest​

New Files Created (37 files)​

Core Orchestration​

  1. .claude/orchestration/__init__.py
  2. .claude/orchestration/agent_discovery.py
  3. .claude/orchestration/message_bus.py
  4. .claude/orchestration/task_queue.py
  5. .claude/orchestration/circuit_breaker.py
  6. .claude/orchestration/retry_engine.py
  7. .claude/orchestration/state_manager.py
  8. .claude/orchestration/monitoring.py

Tests​

  1. .claude/tests/unit/test_agent_discovery.py
  2. .claude/tests/unit/test_message_bus.py
  3. .claude/tests/unit/test_task_queue.py
  4. .claude/tests/unit/test_circuit_breaker.py
  5. .claude/tests/unit/test_retry_engine.py
  6. .claude/tests/unit/test_state_manager.py
  7. .claude/tests/integration/test_autonomous_workflow.py
  8. .claude/tests/integration/test_resilience.py
  9. .claude/tests/e2e/test_full_system.py

Configuration​

  1. .claude/config/rabbitmq.yaml
  2. .claude/config/redis.yaml
  3. .claude/config/prometheus.yaml
  4. .claude/config/grafana/dashboards/system_overview.json
  5. .claude/config/grafana/dashboards/agent_performance.json

Docker/K8s​

  1. .claude/docker/docker-compose.yml
  2. .claude/docker/Dockerfile.orchestrator
  3. .claude/docker/Dockerfile.agent
  4. .claude/k8s/orchestrator-deployment.yaml
  5. .claude/k8s/rabbitmq-statefulset.yaml
  6. .claude/k8s/redis-statefulset.yaml

Documentation​

  1. .claude/docs/AGENT-DISCOVERY.md
  2. .claude/docs/MESSAGE-BUS.md
  3. .claude/docs/TASK-QUEUE.md
  4. .claude/docs/CIRCUIT-BREAKER.md
  5. .claude/docs/RETRY-POLICY.md
  6. .claude/docs/DISTRIBUTED-STATE.md
  7. .claude/docs/OBSERVABILITY.md
  8. .claude/docs/CLI-REFERENCE.md
  9. .claude/docs/DEPLOYMENT-RUNBOOK.md

Total: 37 new files, ~15,000 lines of code

Appendix: Dependencies​

Python Packages (requirements.txt)​

# Core
python>=3.10

# Async
asyncio>=3.4.3
aiohttp>=3.9.0

# Message Bus
aio_pika>=9.3.0

# Redis
redis>=5.0.0
rq>=1.15.0

# Circuit Breaker
pybreaker>=1.0.0

# Monitoring
prometheus-client>=0.19.0
opentelemetry-api>=1.21.0
opentelemetry-sdk>=1.21.0
opentelemetry-exporter-jaeger>=1.21.0

# Logging
python-json-logger>=2.0.7

# State Management
boto3>=1.34.0

# Testing
pytest>=7.4.3
pytest-asyncio>=0.21.1
pytest-cov>=4.1.0
pytest-timeout>=2.2.0
testcontainers>=3.7.1

# Code Quality
black>=23.12.0
mypy>=1.7.1
pylint>=3.0.3

# Documentation
sphinx>=7.2.6
sphinx-rtd-theme>=2.0.0

Infrastructure​

  • RabbitMQ 3.12+
  • Redis 7.0+
  • PostgreSQL 15+ (existing)
  • Prometheus 2.48+
  • Grafana 10.2+
  • Jaeger 1.51+
  • Loki 2.9+

Document Control​

Version History​

VersionDateAuthorChanges
1.0.02025-11-13Orchestrator AgentInitial plan created

Next Review: After Phase 1 completion (Week 2)

Status: ✅ Ready for Execution

END OF PROJECT PLAN​