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Autonomous Multi-Agent Orchestration System - V2 Implementation Plan

Project: CODITECT V2 Autonomous Task Execution Author: AI Orchestrator Created: December 18, 2025 Status: Design Complete - Ready for Implementation Est. Timeline: 8 weeks (160 engineering hours) Target Launch: March 11, 2026 (83 days)

Executive Summary​

Transform CODITECT V2 from human-coordinated to fully autonomous multi-agent system capable of executing 122 tasks across 10 epics with zero human intervention. This system will automatically sync project state, dispatch tasks to specialized agents, monitor execution, and provide real-time progress tracking.

Key Metrics:

  • Current State: 29/122 tasks completed (23%), 93 pending
  • Target State: 95%+ autonomous execution by March 11, 2026
  • Database: context.db with v2_epics, v2_features, v2_tasks, v2_plan_sync
  • Agents Available: 119 specialized agents across 1,716 components

Success Criteria:

  • ✅ Agents execute tasks autonomously without human prompting
  • ✅ Database and markdown stay in sync automatically
  • ✅ Task dependencies resolved correctly (task B waits for task A)
  • ✅ Real-time progress dashboard operational
  • ✅ <5s task dispatch latency
  • ✅ 99.9% uptime with automatic failure recovery

System Architecture​

Core Components​

┌─────────────────────────────────────────────────────────────────┐
│                    AUTONOMOUS ORCHESTRATOR                      │
│                    (Master Controller)                          │
└────────────┬────────────────────────────────────────────────────┘
             │
    ┌────────┴────────┐
    │                 │
    ▼                 ▼
┌─────────┐      ┌──────────────┐
│  Sync   │      │    Task      │
│ Daemon  │◄────►│  Dispatcher  │
└────┬────┘      └───────┬──────┘
     │                   │
     │                   ▼
     │           ┌───────────────┐
     │           │ Agent Executor│
     │           │  (Per-Agent)  │
     │           └───────┬───────┘
     │                   │
     ▼                   ▼
┌─────────────────────────────────┐
│     context.db (SQLite)         │
│  ┌──────────────────────────┐   │
│  │ v2_tasks (122 tasks)     │   │
│  │ v2_features (35 features)│   │
│  │ v2_epics (10 epics)      │   │
│  │ v2_plan_sync (audit log) │   │
│  └──────────────────────────┘   │
└─────────────────────────────────┘
     ▲                   │
     │                   │
     ▼                   ▼
┌─────────────┐   ┌──────────────┐
│  Markdown   │   │   Progress   │
│  Tasklist   │   │  Dashboard   │
└─────────────┘   └──────────────┘

Technology Stack​

Core Infrastructure:

  • Database: SQLite (context.db) - Already operational
  • Message Bus: Python multiprocessing.Queue (Phase 1) → RabbitMQ (Phase 2)
  • Task Queue: Redis + RQ (already available in GKE cluster)
  • State Management: Database-backed with atomic updates
  • Monitoring: Prometheus + Grafana (already operational)

Python Stack:

  • Python 3.10+ (already used)
  • sqlite3 - Database operations
  • redis - Task queue backend
  • pyyaml - Configuration parsing
  • watchdog - File system monitoring
  • Standard library: multiprocessing, threading, queue

Phase Breakdown (8 Weeks)​

Phase 1: Foundation (Weeks 1-2) - P0 CRITICAL​

Goal: Get basic autonomous task execution working

Agent Assignments:

  • backend-development → Build core Python classes
  • database-architect → Optimize database schema/indexes
  • devops-engineer → Setup daemon process management
  • testing-specialist → Create test suite

Deliverables:

  1. sync-daemon.py (Week 1, Days 1-3)

    • Watch V2-TASKLIST-WITH-CHECKBOXES.md for changes
    • Watch context.db for updates
    • Bidirectional sync on change detection
    • Use existing sync-project-plan.py as library
    • Hours: 24h (3 days)
    • Success: Changes to markdown auto-sync to DB within 5 seconds

  2. task-dispatcher.py (Week 1, Days 4-5)

    • Query database for pending P0/P1 tasks
    • Match tasks to agent types via keyword analysis
    • Create priority queue (P0 > P1 > P2)
    • Respect task dependencies
    • Hours: 16h (2 days)
    • Success: Dispatcher correctly assigns 10 test tasks to agents

  3. agent-executor.py (Week 2, Days 1-3)

    • Execute assigned tasks via Claude Code API
    • Update task status (pending → in_progress → completed/blocked)
    • Report results back to database
    • Error handling with retries
    • Hours: 32h (4 days)
    • Success: Single agent completes 1 task end-to-end

  4. Test & Integration (Week 2, Days 4-5)

    • Unit tests for all components
    • Integration test: markdown → DB → dispatcher → executor → completion
    • Hours: 16h (2 days)
    • Success: End-to-end test passes

Phase 1 Total: 88 hours (2 weeks)

Phase 2: Orchestration Controller (Weeks 3-4) - P0​

Goal: Multi-agent coordination with dependency resolution

Agent Assignments:

  • backend-development → Build orchestrator logic
  • application-performance → Optimize for concurrent execution
  • testing-specialist → Load testing

Deliverables:

  1. autonomous-orchestrator.py (Week 3, Days 1-4)

    • Monitor all agent activities
    • Resolve task dependencies (DAG-based)
    • Manage concurrent agent count (max 5-10 parallel)
    • Handle failures and retries (exponential backoff)
    • Create checkpoints at epic/feature milestones
    • Hours: 40h (5 days)
    • Success: Orchestrator coordinates 5 agents simultaneously

  2. Dependency Resolution Engine (Week 3, Day 5 - Week 4, Day 2)

    • Parse task dependencies from descriptions
    • Build directed acyclic graph (DAG)
    • Block dependent tasks until prerequisites complete
    • Detect circular dependencies
    • Hours: 24h (3 days)
    • Success: Task T001.010 waits for T001.009 completion

  3. Progress Dashboard (Week 4, Days 3-5)

    • Real-time web UI showing task progress
    • Epic/feature completion percentages
    • Currently executing agents
    • Recent completions/failures
    • Hours: 24h (3 days)
    • Success: Dashboard shows live updates

Phase 2 Total: 88 hours (2 weeks)

Phase 3: Advanced Features (Weeks 5-6) - P1​

Goal: Production-ready with monitoring and recovery

Agent Assignments:

  • devops-engineer → Infrastructure deployment
  • security-specialist → Security audit
  • codi-documentation-writer → User documentation

Deliverables:

  1. Circuit Breaker & Retry Logic (Week 5, Days 1-2)

    • Implement PyBreaker pattern
    • Exponential backoff (1s, 2s, 4s, 8s, 16s max)
    • Automatic task re-queue on transient failures
    • Hours: 16h (2 days)

  2. Monitoring Integration (Week 5, Days 3-5)

    • Prometheus metrics (tasks_completed, tasks_failed, dispatch_latency)
    • Grafana dashboard integration
    • Alerting on high failure rate
    • Hours: 24h (3 days)

  3. Configuration Management (Week 6, Days 1-2)

    • config/orchestrator-config.json for agent mappings
    • Environment-specific settings (dev/staging/prod)
    • Runtime configuration reload
    • Hours: 16h (2 days)

  4. Documentation (Week 6, Days 3-5)

    • AUTONOMOUS-ORCHESTRATION-GUIDE.md user guide
    • Deployment runbook
    • Troubleshooting playbook
    • Hours: 24h (3 days)

Phase 3 Total: 80 hours (2 weeks)

Phase 4: Deployment & Validation (Weeks 7-8) - P1​

Goal: Deploy to production and validate against V2 goals

Agent Assignments:

  • devops-engineer → Production deployment
  • testing-specialist → Load testing
  • security-specialist → Security validation

Deliverables:

  1. Production Deployment (Week 7, Days 1-3)

    • Deploy to GKE cluster
    • Setup systemd service for daemon
    • Configure Redis task queue
    • Load balancer configuration
    • Hours: 32h (4 days)

  2. Load Testing (Week 7, Days 4-5)

    • Simulate 100+ concurrent tasks
    • Validate <5s dispatch latency
    • Stress test dependency resolution
    • Hours: 16h (2 days)

  3. Security Audit (Week 8, Days 1-2)

    • Review agent execution isolation
    • Audit database access controls
    • Validate no credential leakage
    • Hours: 16h (2 days)

  4. Final Validation (Week 8, Days 3-5)

    • Execute 10 real V2 tasks autonomously
    • Measure success rate (target: 95%+)
    • Performance benchmarking
    • Hours: 24h (3 days)

Phase 4 Total: 88 hours (2 weeks)

Component Specifications​

1. sync-daemon.py​

Purpose: Bidirectional sync between markdown and database

Key Features:

  • File system watcher using watchdog library
  • Database change detection via MAX(updated_at) polling
  • Atomic sync operations (no race conditions)
  • Checksum-based change detection (avoid unnecessary syncs)
  • Graceful degradation if sync fails

Configuration:

sync_daemon:
  markdown_path: "docs/project-management/V2-TASKLIST-WITH-CHECKBOXES.md"
  json_path: "docs/project-management/v2-work-items.json"
  db_path: "submodules/core/coditect-core/context.db"
  watch_interval: 5  # seconds
  sync_debounce: 2   # wait 2s after change before syncing
  max_retries: 3

Example Usage:

# Start daemon
python3 scripts/sync-daemon.py --start

# Stop daemon
python3 scripts/sync-daemon.py --stop

# Status check
python3 scripts/sync-daemon.py --status

# Foreground mode (debugging)
python3 scripts/sync-daemon.py --foreground

Implementation Outline:

class SyncDaemon:
    def __init__(self, config):
        self.markdown_path = config['markdown_path']
        self.db_path = config['db_path']
        self.observer = FileSystemObserver()

    def start(self):
        # Setup file watcher
        self.observer.schedule(handler, self.markdown_path)
        self.observer.start()

        # Start DB polling thread
        db_thread = threading.Thread(target=self.poll_db_changes)
        db_thread.start()

    def on_markdown_change(self, event):
        time.sleep(self.debounce)  # Debounce
        self.sync_plan_to_db()

    def poll_db_changes(self):
        while True:
            current_checksum = self.get_db_checksum()
            if current_checksum != self.last_checksum:
                self.sync_db_to_plan()
                self.last_checksum = current_checksum
            time.sleep(self.watch_interval)

2. task-dispatcher.py​

Purpose: Intelligent task assignment to specialized agents

Agent Type Mappings:

AGENT_MAPPINGS = {
    # Infrastructure & DevOps
    "infrastructure|deployment|docker|kubernetes|gke|argocd": "devops-engineer",
    "database|schema|migration|fdb|postgresql|redis": "database-architect",

    # Security
    "security|authentication|oauth|jwt|secret|compliance|audit": "security-specialist",
    "penetration|vulnerability|sast|dast": "security-specialist",

    # Development
    "api|endpoint|backend|handler|middleware": "backend-development",
    "ui|frontend|component|react|typescript|toon": "frontend-development-agent",
    "test|validation|coverage|pytest": "testing-specialist",

    # Quality & Performance
    "performance|optimization|latency|throughput": "application-performance",
    "refactor|clean|tech debt": "codebase-refactoring-specialist",

    # Documentation & Knowledge
    "documentation|guide|readme|tutorial": "codi-documentation-writer",
    "onboarding|training|education": "coditect-onboarding",

    # Specialized Systems
    "workflow|automation|bpmn|state machine": "workflow-automation-specialist",
    "monitoring|metrics|prometheus|grafana|observability": "observability-platform",
    "license|subscription|billing|stripe": "license-management-specialist",
}

Priority Queue Logic:

def get_next_task():
    """
    Query database for next available task.
    Priority: P0 > P1 > P2 > P3
    Filter: status='pending', no blocking dependencies
    """
    query = """
        SELECT t.* FROM v2_tasks t
        WHERE t.status = 'pending'
          AND NOT EXISTS (
              SELECT 1 FROM task_dependencies d
              JOIN v2_tasks dt ON d.dependency_task_id = dt.task_id
              WHERE d.task_id = t.task_id
                AND dt.status != 'completed'
          )
        ORDER BY
          CASE t.priority
            WHEN 'P0' THEN 0
            WHEN 'P1' THEN 1
            WHEN 'P2' THEN 2
            ELSE 3
          END,
          t.created_at ASC
        LIMIT 1
    """

Task Assignment Algorithm:

def assign_task_to_agent(task):
    description = task['description'].lower()

    # Check each mapping pattern
    for pattern, agent_type in AGENT_MAPPINGS.items():
        if re.search(pattern, description):
            return agent_type

    # Fallback to epic context
    epic_id = get_epic_for_task(task['task_id'])
    if epic_id == 'E001':
        return 'backend-development'  # Core Platform
    elif epic_id == 'E008':
        return 'security-specialist'  # Security

    # Final fallback: general-purpose agent
    return 'general-purpose'

3. agent-executor.py​

Purpose: Execute tasks via specialized agents with status tracking

Execution Flow:

class AgentExecutor:
    def __init__(self, agent_type, task_id):
        self.agent_type = agent_type
        self.task_id = task_id

    def execute(self):
        # 1. Mark task as in_progress
        self.update_status('in_progress')

        # 2. Build agent prompt
        prompt = self.build_agent_prompt()

        # 3. Invoke agent via Claude Code
        result = self.invoke_agent(prompt)

        # 4. Parse result
        if result['success']:
            self.update_status('completed')
            self.record_completion(result)
        else:
            self.update_status('blocked')
            self.record_failure(result['error'])

    def build_agent_prompt(self):
        task = self.get_task_details()

        return f"""
        You are {self.agent_type} specialized agent.

        Task: {task['description']}
        Priority: {task['priority']}
        Estimated Hours: {task['estimated_hours']}

        Context:
        - Epic: {task['epic_name']}
        - Feature: {task['feature_name']}
        - Dependencies: {task['dependencies']}

        Execute this task and report:
        1. What you did
        2. Files changed
        3. Tests run
        4. Completion status

        Follow CODITECT best practices and update relevant documentation.
        """

Agent Invocation Methods:

  1. Direct Python API (preferred):
def invoke_agent(self, prompt):
    # Use Claude Code Python API
    from anthropic import Anthropic
    client = Anthropic()

    response = client.messages.create(
        model="claude-sonnet-4-5",
        max_tokens=8000,
        messages=[{
            "role": "user",
            "content": prompt
        }],
        tools=get_claude_code_tools()
    )

    return parse_agent_response(response)
  1. CLI Invocation (alternative):
def invoke_agent(self, prompt):
    # Write prompt to file
    with open(f'/tmp/agent_task_{self.task_id}.txt', 'w') as f:
        f.write(prompt)

    # Invoke Claude Code CLI
    result = subprocess.run([
        'claude',
        '--prompt-file', f'/tmp/agent_task_{self.task_id}.txt',
        '--output-format', 'json'
    ], capture_output=True, text=True)

    return json.loads(result.stdout)

4. autonomous-orchestrator.py​

Purpose: Master controller coordinating all system components

Core Responsibilities:

  1. Start/stop sync daemon
  2. Manage task dispatcher pool
  3. Monitor agent executors
  4. Handle failures and retries
  5. Create checkpoints at milestones
  6. Provide progress reporting

System State Management:

class OrchestratorState:
    def __init__(self):
        self.active_agents = {}      # task_id -> executor instance
        self.pending_queue = []      # tasks waiting for dependencies
        self.failed_tasks = []       # tasks that failed (for retry)
        self.checkpoints = []        # milestone checkpoints

    def get_metrics(self):
        return {
            'total_tasks': self.query_db('SELECT COUNT(*) FROM v2_tasks'),
            'completed': self.query_db("SELECT COUNT(*) FROM v2_tasks WHERE status='completed'"),
            'in_progress': len(self.active_agents),
            'pending': self.query_db("SELECT COUNT(*) FROM v2_tasks WHERE status='pending'"),
            'blocked': len(self.failed_tasks),
            'uptime': time.time() - self.start_time,
            'tasks_per_hour': self.completed_count / (uptime_hours or 1)
        }

Main Control Loop:

def run(self):
    # Start sync daemon
    self.sync_daemon.start()

    while self.running:
        # 1. Check for available tasks
        if len(self.active_agents) < self.max_concurrent:
            task = self.dispatcher.get_next_task()

            if task:
                # 2. Spawn agent executor
                executor = AgentExecutor(
                    agent_type=self.dispatcher.assign_agent(task),
                    task_id=task['task_id']
                )

                self.active_agents[task['task_id']] = executor
                executor.start()  # Run in separate thread

        # 3. Monitor active agents
        for task_id, executor in list(self.active_agents.items()):
            if executor.is_complete():
                del self.active_agents[task_id]
                self.handle_completion(executor)

        # 4. Retry failed tasks
        self.retry_failed_tasks()

        # 5. Check for milestone checkpoints
        self.check_milestones()

        # 6. Sleep briefly
        time.sleep(1)

Failure Handling:

def handle_completion(self, executor):
    if executor.success:
        # Log completion
        logger.info(f"Task {executor.task_id} completed by {executor.agent_type}")

        # Check for dependent tasks
        self.unblock_dependent_tasks(executor.task_id)

    else:
        # Record failure
        self.failed_tasks.append({
            'task_id': executor.task_id,
            'error': executor.error,
            'retry_count': executor.retry_count,
            'failed_at': datetime.now(timezone.utc)
        })

        # Schedule retry
        if executor.retry_count < self.max_retries:
            self.schedule_retry(executor.task_id, executor.retry_count + 1)

Configuration File Structure​

config/orchestrator-config.json​

{
  "version": "1.0.0",
  "orchestrator": {
    "max_concurrent_agents": 5,
    "max_retries": 3,
    "retry_backoff_base": 2,
    "checkpoint_interval_tasks": 10,
    "health_check_interval": 30
  },

  "sync_daemon": {
    "enabled": true,
    "markdown_path": "docs/project-management/V2-TASKLIST-WITH-CHECKBOXES.md",
    "json_path": "docs/project-management/v2-work-items.json",
    "db_path": "submodules/core/coditect-core/context.db",
    "watch_interval": 5,
    "sync_debounce": 2
  },

  "task_dispatcher": {
    "batch_size": 10,
    "priority_weights": {
      "P0": 100,
      "P1": 50,
      "P2": 25,
      "P3": 10
    }
  },

  "agent_mappings": {
    "infrastructure|deployment|docker|kubernetes": "devops-engineer",
    "security|authentication|compliance": "security-specialist",
    "api|endpoint|backend": "backend-development",
    "ui|frontend|component|react": "frontend-development-agent",
    "test|validation|coverage": "testing-specialist",
    "documentation|guide|readme": "codi-documentation-writer",
    "performance|optimization": "application-performance",
    "database|schema|migration": "database-architect"
  },

  "monitoring": {
    "prometheus_enabled": true,
    "prometheus_port": 9090,
    "grafana_dashboard_id": "coditect-orchestrator",
    "alert_on_failure_rate": 0.1
  },

  "logging": {
    "level": "INFO",
    "file": "logs/orchestrator.log",
    "max_bytes": 10485760,
    "backup_count": 5
  }
}

Database Schema Enhancements​

New Table: task_dependencies​

CREATE TABLE IF NOT EXISTS task_dependencies (
    dependency_id INTEGER PRIMARY KEY AUTOINCREMENT,
    task_id TEXT NOT NULL,
    dependency_task_id TEXT NOT NULL,
    dependency_type TEXT DEFAULT 'blocks',  -- blocks, soft_dependency
    created_at TEXT DEFAULT (datetime('now', 'utc')),
    FOREIGN KEY (task_id) REFERENCES v2_tasks(task_id),
    FOREIGN KEY (dependency_task_id) REFERENCES v2_tasks(task_id),
    UNIQUE(task_id, dependency_task_id)
);

CREATE INDEX idx_task_deps_task ON task_dependencies(task_id);
CREATE INDEX idx_task_deps_dependency ON task_dependencies(dependency_task_id);

New Table: orchestrator_state​

CREATE TABLE IF NOT EXISTS orchestrator_state (
    state_id INTEGER PRIMARY KEY AUTOINCREMENT,
    timestamp TEXT DEFAULT (datetime('now', 'utc')),
    active_agents INTEGER DEFAULT 0,
    pending_tasks INTEGER DEFAULT 0,
    completed_tasks INTEGER DEFAULT 0,
    failed_tasks INTEGER DEFAULT 0,
    tasks_per_hour REAL DEFAULT 0,
    uptime_seconds INTEGER DEFAULT 0,
    metrics_json TEXT,  -- JSON blob with detailed metrics

    CHECK(active_agents >= 0),
    CHECK(pending_tasks >= 0)
);

CREATE INDEX idx_orchestrator_state_timestamp ON orchestrator_state(timestamp);

New Table: agent_execution_log​

CREATE TABLE IF NOT EXISTS agent_execution_log (
    log_id INTEGER PRIMARY KEY AUTOINCREMENT,
    task_id TEXT NOT NULL,
    agent_type TEXT NOT NULL,
    started_at TEXT DEFAULT (datetime('now', 'utc')),
    completed_at TEXT,
    status TEXT DEFAULT 'running',  -- running, completed, failed, timeout
    exit_code INTEGER,
    stdout TEXT,
    stderr TEXT,
    execution_time_seconds REAL,
    retry_count INTEGER DEFAULT 0,

    FOREIGN KEY (task_id) REFERENCES v2_tasks(task_id)
);

CREATE INDEX idx_agent_log_task ON agent_execution_log(task_id);
CREATE INDEX idx_agent_log_agent_type ON agent_execution_log(agent_type);
CREATE INDEX idx_agent_log_status ON agent_execution_log(status, started_at);

Dependency Resolution Strategy​

Automatic Dependency Detection​

Parse task descriptions for dependency keywords:

  • "after", "once", "requires", "depends on", "blocked by"
  • Task ID references: "T001.009", "T008.001"

Example:

Task: T001.010 "Implement task queue manager (Redis + RQ)"
Description: "... after deploying RabbitMQ message bus (T001.009)"

→ Auto-detect: T001.010 depends on T001.009

Dependency Graph Construction​

class DependencyGraph:
    def __init__(self):
        self.graph = defaultdict(list)  # task_id -> [dependent_task_ids]

    def add_dependency(self, task_id, depends_on):
        self.graph[depends_on].append(task_id)

    def get_ready_tasks(self):
        """Return tasks with all dependencies satisfied."""
        ready = []
        for task_id in self.all_tasks:
            if self.is_ready(task_id):
                ready.append(task_id)
        return ready

    def is_ready(self, task_id):
        """Check if all dependencies are completed."""
        dependencies = self.get_dependencies(task_id)
        for dep_id in dependencies:
            dep_task = get_task(dep_id)
            if dep_task['status'] != 'completed':
                return False
        return True

    def detect_cycles(self):
        """Detect circular dependencies using DFS."""
        visited = set()
        rec_stack = set()

        def dfs(node):
            visited.add(node)
            rec_stack.add(node)

            for neighbor in self.graph.get(node, []):
                if neighbor not in visited:
                    if dfs(neighbor):
                        return True
                elif neighbor in rec_stack:
                    return True  # Cycle detected!

            rec_stack.remove(node)
            return False

        for task_id in self.all_tasks:
            if task_id not in visited:
                if dfs(task_id):
                    return True  # Cycle exists
        return False

Progress Dashboard Specification​

Real-Time Web UI​

Technology: FastAPI + WebSockets + Vue.js

Features:

  1. Overview Panel

    • Total tasks: 122
    • Completed: 29 (23%)
    • In Progress: 5 agents active
    • Pending: 93
    • Progress bar with animation

  2. Epic Breakdown

    • E001 Core Platform: 2/16 (12%) ⚡ Active
    • E008 Security: 0/15 (0%) ⚡ Active
    • E010 Rollout: 3/20 (15%) ⚡ Active
    • (Visual progress bars for each)

  3. Active Agents Panel

    • devops-engineer → T001.005 (32h est.) - 45% complete
    • security-specialist → T008.001 (24h est.) - 20% complete
    • backend-development → T001.010 (32h est.) - 60% complete

  4. Recent Activity Log

    • [10:23:45] Task T001.003 completed by backend-development ✅
    • [10:15:12] Task T001.002 started by backend-development ⏳
    • [09:58:34] Task T001.001 completed by backend-development ✅

  5. Metrics Panel

    • Tasks per hour: 1.2
    • Average completion time: 3.5 hours
    • Success rate: 95%
    • Uptime: 99.9%

API Endpoints:

@app.get("/api/status")
async def get_status():
    return {
        'total_tasks': 122,
        'completed': query_db("SELECT COUNT(*) FROM v2_tasks WHERE status='completed'"),
        'in_progress': len(orchestrator.active_agents),
        'pending': query_db("SELECT COUNT(*) FROM v2_tasks WHERE status='pending'"),
        'uptime': orchestrator.uptime
    }

@app.websocket("/ws/progress")
async def websocket_progress(websocket: WebSocket):
    await websocket.accept()
    while True:
        data = orchestrator.get_metrics()
        await websocket.send_json(data)
        await asyncio.sleep(1)

Testing Strategy​

Unit Tests (Week 2)​

Test Coverage:

  • test_sync_daemon.py - File watching, sync logic
  • test_task_dispatcher.py - Agent assignment, priority queue
  • test_agent_executor.py - Task execution, status updates
  • test_dependency_resolver.py - DAG construction, cycle detection
  • test_orchestrator.py - Main control loop, error handling

Example Test:

def test_task_assignment():
    """Test task dispatcher assigns correct agent types."""

    # Test infrastructure task
    task = {'description': 'Deploy RabbitMQ message bus', 'task_id': 'T001.009'}
    agent = dispatcher.assign_task_to_agent(task)
    assert agent == 'devops-engineer'

    # Test security task
    task = {'description': 'Setup GCP Identity Platform', 'task_id': 'T008.001'}
    agent = dispatcher.assign_task_to_agent(task)
    assert agent == 'security-specialist'

    # Test fallback
    task = {'description': 'Miscellaneous task', 'task_id': 'T999.999'}
    agent = dispatcher.assign_task_to_agent(task)
    assert agent == 'general-purpose'

Integration Tests (Week 2)​

End-to-End Scenarios:

  1. Happy Path: Markdown update → DB sync → Task dispatch → Agent execution → Completion
  2. Dependency Resolution: Task A blocks Task B → A completes → B automatically starts
  3. Failure Recovery: Agent fails → Retry with backoff → Success on retry 2
  4. Concurrent Execution: 5 agents run simultaneously without conflicts

Load Tests (Week 7)​

Scenarios:

  • 100 tasks in queue → Measure dispatch latency (target: <5s)
  • 10 concurrent agents → Measure resource usage
  • 1000 rapid markdown updates → Measure sync stability

Deployment Plan​

Development Environment (Week 1-6)​

Local Setup:

# 1. Install dependencies
pip install watchdog redis rq prometheus-client

# 2. Initialize database schema
python3 scripts/sync-project-plan.py --init
sqlite3 context.db < scripts/init-orchestrator-schema.sql

# 3. Start Redis (for task queue)
docker run -d -p 6379:6379 redis:7-alpine

# 4. Start orchestrator in foreground
python3 scripts/autonomous-orchestrator.py --foreground --verbose

Production Deployment (Week 7)​

GKE Cluster:

# deployment/orchestrator-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: coditect-orchestrator
spec:
  replicas: 1  # Single instance (uses SQLite)
  template:
    spec:
      containers:
      - name: orchestrator
        image: gcr.io/coditect-prod/orchestrator:latest
        env:
        - name: DB_PATH
          value: /data/context.db
        - name: REDIS_HOST
          value: redis-service
        - name: PROMETHEUS_PORT
          value: "9090"
        volumeMounts:
        - name: data
          mountPath: /data
        - name: markdown
          mountPath: /markdown
      volumes:
      - name: data
        persistentVolumeClaim:
          claimName: orchestrator-data-pvc
      - name: markdown
        hostPath:
          path: /mnt/docs/project-management

Systemd Service (Alternative):

[Unit]
Description=CODITECT Autonomous Orchestrator
After=network.target redis.service

[Service]
Type=simple
User=coditect
WorkingDirectory=/opt/coditect
ExecStart=/usr/bin/python3 /opt/coditect/scripts/autonomous-orchestrator.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

Monitoring & Observability​

Prometheus Metrics​

Exposed Metrics:

from prometheus_client import Counter, Gauge, Histogram

# Task metrics
tasks_total = Counter('coditect_tasks_total', 'Total tasks processed')
tasks_completed = Counter('coditect_tasks_completed', 'Tasks completed successfully')
tasks_failed = Counter('coditect_tasks_failed', 'Tasks failed')

# Agent metrics
active_agents = Gauge('coditect_active_agents', 'Number of active agents')
dispatch_latency = Histogram('coditect_dispatch_latency_seconds', 'Task dispatch latency')

# System metrics
orchestrator_uptime = Gauge('coditect_orchestrator_uptime_seconds', 'Orchestrator uptime')
sync_operations = Counter('coditect_sync_operations_total', 'Total sync operations')

Grafana Dashboard​

Panels:

  1. Task Completion Rate (tasks/hour)
  2. Active Agents (gauge)
  3. Dispatch Latency (p50, p95, p99)
  4. Failure Rate (percentage)
  5. Epic Progress (stacked bar chart)

Alerting Rules​

groups:
- name: orchestrator
  rules:
  - alert: HighTaskFailureRate
    expr: rate(coditect_tasks_failed[5m]) > 0.1
    for: 5m
    annotations:
      summary: "High task failure rate detected"

  - alert: OrchestratorDown
    expr: up{job="orchestrator"} == 0
    for: 1m
    annotations:
      summary: "Orchestrator is down"

  - alert: DispatchLatencyHigh
    expr: histogram_quantile(0.95, coditect_dispatch_latency_seconds) > 5
    for: 5m
    annotations:
      summary: "Dispatch latency exceeds 5 seconds"

Risk Mitigation​

Critical Risks​

RiskProbabilityImpactMitigation
Database corruptionLowHighAutomatic backups every 6 hours
Agent execution timeoutMediumMedium2-hour timeout with automatic kill
Circular dependenciesLowHighValidation on dependency creation
Out of sync (markdown ↔ DB)MediumMediumChecksums + manual reconciliation tool
Claude Code API rate limitsHighHighRate limiting + queue backoff

Failure Scenarios​

Scenario 1: Database Locked

  • Cause: Concurrent writes from sync daemon + executor
  • Detection: SQLite error "database is locked"
  • Recovery: Retry with exponential backoff, max 5 retries
  • Prevention: Use WAL mode: PRAGMA journal_mode=WAL

Scenario 2: Agent Hangs

  • Cause: Agent stuck in infinite loop or waiting for user input
  • Detection: Execution time > 2 hours (configurable)
  • Recovery: Kill agent process, mark task as 'timeout'
  • Prevention: Agent execution timeout wrapper

Scenario 3: Dependency Deadlock

  • Cause: Task A depends on Task B, Task B depends on Task A
  • Detection: Cycle detection algorithm during dependency creation
  • Recovery: Reject dependency creation, alert operator
  • Prevention: Topological sort validation

Success Metrics (KPIs)​

Primary Metrics​

MetricTargetCurrentMeasurement
Autonomy Rate95%0%Tasks completed without human intervention
Dispatch Latency<5sN/ATime from task ready → agent assignment
Task Throughput10/hourN/ACompleted tasks per hour
Success Rate95%N/ACompleted / (Completed + Failed)
Uptime99.9%N/AOrchestrator availability

Secondary Metrics​

  • Average task completion time
  • Agent utilization rate (active time / total time)
  • Sync operations per hour
  • Checkpoint frequency

Progress Tracking​

Weekly Goals:

  • Week 2: 5 tasks completed autonomously
  • Week 4: 20 tasks completed autonomously
  • Week 6: 50 tasks completed autonomously
  • Week 8: 80+ tasks completed autonomously (65% of total)

Timeline & Milestones​

Gantt Chart Summary​

Week 1-2: Foundation
├─ sync-daemon.py          ████████░░ (80% complete by end)
├─ task-dispatcher.py      ████████░░
├─ agent-executor.py       ██████░░░░
└─ Integration testing     ████░░░░░░

Week 3-4: Orchestration
├─ autonomous-orchestrator ██████████
├─ Dependency resolution   ████████░░
└─ Progress dashboard      ██████░░░░

Week 5-6: Advanced Features
├─ Circuit breaker         ████████░░
├─ Monitoring integration  ██████████
└─ Documentation           ████████░░

Week 7-8: Deployment
├─ Production deploy       ████████░░
├─ Load testing            ██████░░░░
├─ Security audit          ████████░░
└─ Final validation        ██████████

Key Milestones​

  • Week 2 (Jan 1): First autonomous task completion
  • Week 4 (Jan 15): 20 tasks completed, dependency resolution working
  • Week 6 (Jan 29): Monitoring operational, circuit breaker tested
  • Week 8 (Feb 12): Production deployment, 80+ tasks autonomous

Budget & Resources​

Engineering Time​

PhaseHoursFTE (at 40h/week)Cost @ $150/h
Phase 188h1.1 weeks$13,200
Phase 288h1.1 weeks$13,200
Phase 380h1.0 weeks$12,000
Phase 488h1.1 weeks$13,200
Total344h4.3 weeks$51,600

Infrastructure Costs​

ResourceMonthly Cost2-Month Cost
Redis (GKE)$30$60
Prometheus$20$40
Grafana Cloud$50$100
Total$100/mo$200

Grand Total: $51,800 (one-time engineering) + $100/mo (ongoing)

Next Steps​

Immediate Actions (This Week)​

  1. Review & Approve Plan (1 day)

    • Stakeholder review of this document
    • Budget approval
    • Resource allocation

  2. Setup Development Environment (2 days)

    • Clone coditect-rollout-master
    • Initialize database schema
    • Install dependencies
    • Start Redis

  3. Begin Phase 1 (Week 1)

    • Create sync-daemon.py skeleton
    • Implement file watching
    • Test with 5 sample tasks

Week 1 Deliverables​

  • sync-daemon.py functional (watches markdown + DB)
  • task-dispatcher.py assigns 10 test tasks correctly
  • Unit tests passing (20+ tests)
  • Documentation: Architecture diagram updated

Week 2 Checkpoint​

  • First autonomous task completion (T001.001)
  • Integration test: markdown → DB → dispatch → execute → complete
  • Progress dashboard prototype deployed
  • Team demo: Show 3 tasks completing autonomously

Appendix​

A. Full Agent Type Catalog​

Available Agents (119 total):

Development (25 agents):

  • backend-development, frontend-development-agent, fullstack-development
  • api-development-specialist, microservices-architect
  • mobile-app-development, native-app-development
  • progressive-web-app-development

Infrastructure (18 agents):

  • devops-engineer, cloud-architect, kubernetes-specialist
  • docker-containerization, ci-cd-pipeline
  • infrastructure-as-code, terraform-automation
  • ansible-automation, serverless-architect

Security (12 agents):

  • security-specialist, application-security
  • cloud-security, network-security
  • identity-access-management, secrets-management
  • security-compliance, penetration-testing

Testing (15 agents):

  • testing-specialist, test-automation
  • performance-testing, load-testing
  • security-testing, integration-testing
  • e2e-testing, api-testing

Data (10 agents):

  • database-architect, data-engineering
  • data-pipeline, etl-specialist
  • data-warehouse, analytics-platform

Quality (8 agents):

  • code-review-specialist, codebase-refactoring-specialist
  • technical-debt-manager, application-performance
  • observability-platform

Documentation (6 agents):

  • codi-documentation-writer, technical-writer
  • api-documentation, user-guide-writer

Other (25 agents):

  • project-manager-orchestrator, scrum-master
  • product-owner, business-analyst
  • ui-ux-designer, accessibility-specialist
  • ...and more

B. Task Dependency Examples​

Explicit Dependencies (from markdown):

T001.010: Implement task queue manager (Redis + RQ)
  → Depends on: T001.009 (Deploy RabbitMQ)

T001.014: Test agent-to-agent task delegation
  → Depends on: T001.009, T001.010, T001.011 (all communication infra)

T003.002: Implement dual-write (SQLite → FDB) pattern
  → Blocked by: "Infrastructure FDB operational"

Implicit Dependencies (from descriptions):

T008.006: Implement JWT middleware
  → Likely depends on T008.002 (Configure OAuth2/OIDC)

T010.011: Onboard Pilot Phase 2 customers
  → Depends on T010.009 (Pilot Phase 1 support complete)

C. Configuration Templates​

Agent Priority Overrides:

{
  "agent_priority_overrides": {
    "T008.001": "security-specialist",  // Force specific agent
    "T001.009": "devops-engineer",
    "T002.003": "frontend-development-agent"
  }
}

Concurrent Agent Limits:

{
  "agent_limits": {
    "max_total": 10,
    "max_per_type": {
      "security-specialist": 2,
      "devops-engineer": 3,
      "backend-development": 5
    }
  }
}

Document Metadata​

Version: 1.0.0 Status: Design Complete - Ready for Implementation Author: AI Orchestrator (Claude Sonnet 4.5) Created: December 18, 2025 Last Updated: December 18, 2025 Review Cycle: Weekly during implementation

Stakeholders:

  • Hal Casteel - Founder/CEO/CTO (Approval Authority)
  • Backend Development Team - Implementation
  • DevOps Team - Deployment
  • Security Team - Audit

Related Documents:

  • V2-CONSOLIDATED-PROJECT-PLAN.md
  • V2-TASKLIST-WITH-CHECKBOXES.md
  • ORCHESTRATOR-PROJECT-PLAN.md
  • AUTONOMOUS-AGENT-SYSTEM-DESIGN.md

END OF DOCUMENT