Claude Force Performance Optimization Implementation Plan — claude-force 2.2.0 documentation

# Claude Force Performance Optimization Implementation Plan

Version: 1.0 Date: 2025-11-14 Status: Draft - Pending Approval Target Release: v2.3.0

## Executive Summary

This document outlines a comprehensive plan to optimize Claude Force performance based on the findings in the Performance Analysis Report. The implementation is divided into three phases over 3 months, targeting:

### Primary Goals

Metric | Current | Target | Improvement
--- | --- | --- | ---
Workflow Execution Time | 12-30s (3 agents) | 4-10s | 50-80% faster
Cost Per Execution | Baseline | 30-50% less | Cost reduction
Throughput | 1 task/time | 2-5 tasks/time | 2-5x increase
Cache Hit Rate | 0% | 20-70% | New capability

### Implementation Phases

Phase 1 (Month 1): Foundation ✅ High Impact

- Async API client implementation

- Response caching system

- Backward compatibility layer

Phase 2 (Month 2): Advanced Optimization ⭐ Medium Impact

- Parallel workflow execution (DAG-based)

- Metrics aggregation

- Query result caching

Phase 3 (Month 3): Polish & Enhancement 📊 Low Impact

- Performance monitoring enhancements

- Circuit breakers

- Advanced caching strategies

### Expected ROI

    Investment: ~40-60 development hours
    Benefits:
      - 50-80% faster workflow execution → Better UX
      - 30-50% cost reduction → $500-2000/month savings (depending on usage)
      - 2-5x throughput → Support for concurrent users
      - Production-ready scalability → Enterprise readiness

## Table of Contents

1. Phase 1: Foundation Optimizations

2. Phase 2: Advanced Optimizations

3. Phase 3: Polish & Enhancement

4. Testing Strategy

5. Migration & Backward Compatibility

6. Risk Assessment

7. Success Metrics

8. Implementation Timeline

## Phase 1: Foundation Optimizations

Duration: 3-4 weeks Priority: 🔴 CRITICAL Expected Impact: 50-80% performance improvement

### 1.1 Async API Client Implementation

#### Objective

Replace synchronous Claude API calls with async implementation to enable non-blocking operations and concurrent execution.

#### Current State

    # orchestrator.py (synchronous)
    response = self.client.messages.create(
        model=model,
        max_tokens=max_tokens,
        temperature=temperature,
        messages=[{"role": "user", "content": prompt}]
    )

#### Target State

    # orchestrator.py (async)
    async def execute_agent_async(self, agent_name: str, task: str):
        response = await self.async_client.messages.create(
            model=model,
            max_tokens=max_tokens,
            temperature=temperature,
            messages=[{"role": "user", "content": prompt}]
        )
        return response

#### Implementation Steps

Step 1: Install async dependencies

    # Update requirements.txt
    anthropic>=0.40.0  # Already supports async
    aiofiles>=23.0.0   # Async file I/O

Step 2: Create async orchestrator module

Create `claude_force/async_orchestrator.py`:

    """
    Async version of AgentOrchestrator for non-blocking operations.
    """
    import asyncio
    import aiofiles
    from pathlib import Path
    from typing import Optional, Dict, Any
    from anthropic import AsyncAnthropic
    from .performance_tracker import PerformanceTracker
    from .agent_memory import AgentMemory

    class AsyncAgentOrchestrator:
        """Async orchestrator for non-blocking agent execution."""

        def __init__(self, config_path: Optional[Path] = None, api_key: Optional[str] = None):
            self.config_path = config_path or Path.home() / ".claude" / "claude.json"
            self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")

            # Lazy initialization
            self._async_client: Optional[AsyncAnthropic] = None
            self._config: Optional[Dict] = None
            self._performance_tracker: Optional[PerformanceTracker] = None
            self._agent_memory: Optional[AgentMemory] = None

        @property
        def async_client(self) -> AsyncAnthropic:
            """Lazy-load async client."""
            if self._async_client is None:
                self._async_client = AsyncAnthropic(api_key=self.api_key)
            return self._async_client

        async def load_config(self) -> Dict:
            """Load configuration asynchronously."""
            if self._config is None:
                async with aiofiles.open(self.config_path, 'r') as f:
                    content = await f.read()
                    self._config = json.loads(content)
            return self._config

        async def load_agent_definition(self, agent_name: str) -> str:
            """Load agent definition asynchronously."""
            config = await self.load_config()
            agent_config = config['agents'].get(agent_name)

            if not agent_config:
                raise ValueError(f"Agent '{agent_name}' not found")

            agent_file = self.config_path.parent / agent_config['path']

            async with aiofiles.open(agent_file, 'r') as f:
                return await f.read()

        async def execute_agent(
            self,
            agent_name: str,
            task: str,
            model: Optional[str] = None,
            max_tokens: int = 4096,
            temperature: float = 0.7
        ) -> str:
            """Execute agent asynchronously."""
            import time
            from datetime import datetime

            start_time = time.time()

            try:
                # Load agent definition
                agent_definition = await self.load_agent_definition(agent_name)

                # Build prompt
                prompt = f"{agent_definition}\n\n# Task\n{task}"

                # Call API asynchronously
                response = await self.async_client.messages.create(
                    model=model or "claude-3-5-haiku-20241022",
                    max_tokens=max_tokens,
                    temperature=temperature,
                    messages=[{"role": "user", "content": prompt}]
                )

                # Extract result
                result = response.content[0].text

                # Track performance
                execution_time = (time.time() - start_time) * 1000
                self._track_performance(
                    agent_name=agent_name,
                    task=task,
                    success=True,
                    execution_time_ms=execution_time,
                    model=response.model,
                    input_tokens=response.usage.input_tokens,
                    output_tokens=response.usage.output_tokens
                )

                return result

            except Exception as e:
                execution_time = (time.time() - start_time) * 1000
                self._track_performance(
                    agent_name=agent_name,
                    task=task,
                    success=False,
                    execution_time_ms=execution_time,
                    error_type=type(e).__name__
                )
                raise

        async def execute_multiple(
            self,
            tasks: list[tuple[str, str]]  # List of (agent_name, task)
        ) -> list[str]:
            """Execute multiple agents concurrently."""
            results = await asyncio.gather(*[
                self.execute_agent(agent_name, task)
                for agent_name, task in tasks
            ])
            return results

        def _track_performance(self, **kwargs):
            """Track performance metrics (sync operation)."""
            if self._performance_tracker is None:
                self._performance_tracker = PerformanceTracker()
            # Sync operation - acceptable for metrics
            self._performance_tracker.track_execution(**kwargs)

Step 3: Add backward compatibility wrapper

Update `claude_force/orchestrator.py`:

    class AgentOrchestrator:
        """Synchronous orchestrator with async support."""

        def __init__(self, ...):
            # Existing sync initialization
            self._async_orchestrator = None

        def execute_agent(self, agent_name: str, task: str, **kwargs) -> str:
            """Synchronous execution (backward compatible)."""
            # Existing synchronous implementation
            pass

        async def execute_agent_async(self, agent_name: str, task: str, **kwargs) -> str:
            """Asynchronous execution (new feature)."""
            if self._async_orchestrator is None:
                from .async_orchestrator import AsyncAgentOrchestrator
                self._async_orchestrator = AsyncAgentOrchestrator(
                    config_path=self.config_path,
                    api_key=self.api_key
                )
            return await self._async_orchestrator.execute_agent(agent_name, task, **kwargs)

        async def execute_multiple_async(self, tasks: list) -> list:
            """Execute multiple agents concurrently."""
            if self._async_orchestrator is None:
                from .async_orchestrator import AsyncAgentOrchestrator
                self._async_orchestrator = AsyncAgentOrchestrator(
                    config_path=self.config_path,
                    api_key=self.api_key
                )
            return await self._async_orchestrator.execute_multiple(tasks)

Step 4: Update CLI for async support

Add async commands to `claude_force/cli.py`:

    @click.command()
    @click.argument('agent_name')
    @click.argument('task')
    @click.option('--async', 'use_async', is_flag=True, help='Use async execution')
    def execute(agent_name: str, task: str, use_async: bool):
        """Execute an agent with a task."""
        orchestrator = AgentOrchestrator()

        if use_async:
            # Run async version
            result = asyncio.run(orchestrator.execute_agent_async(agent_name, task))
        else:
            # Run sync version (backward compatible)
            result = orchestrator.execute_agent(agent_name, task)

        click.echo(result)

#### Testing Requirements

Unit Tests:

    # tests/test_async_orchestrator.py
    import pytest
    import asyncio
    from claude_force.async_orchestrator import AsyncAgentOrchestrator

    @pytest.mark.asyncio
    async def test_async_execute_agent():
        """Test async agent execution."""
        orchestrator = AsyncAgentOrchestrator()
        result = await orchestrator.execute_agent("python-expert", "What are decorators?")
        assert result
        assert isinstance(result, str)

    @pytest.mark.asyncio
    async def test_concurrent_execution():
        """Test concurrent agent execution."""
        orchestrator = AsyncAgentOrchestrator()

        tasks = [
            ("python-expert", "Explain lists"),
            ("python-expert", "Explain dicts"),
            ("python-expert", "Explain sets")
        ]

        import time
        start = time.time()
        results = await orchestrator.execute_multiple(tasks)
        elapsed = time.time() - start

        assert len(results) == 3
        assert all(isinstance(r, str) for r in results)

        # Should be faster than sequential execution
        # (concurrent should be ~similar to single execution time)
        # Not 3x the time of a single execution
        print(f"Concurrent execution: {elapsed:.2f}s")

Integration Tests:

    # tests/integration/test_async_workflows.py
    @pytest.mark.asyncio
    async def test_workflow_async_speedup():
        """Verify async workflow is faster than sync."""
        from claude_force.orchestrator import AgentOrchestrator
        import time

        orchestrator = AgentOrchestrator()

        tasks = [
            ("python-expert", "Task 1"),
            ("python-expert", "Task 2"),
            ("python-expert", "Task 3")
        ]

        # Sync execution
        sync_start = time.time()
        for agent, task in tasks:
            orchestrator.execute_agent(agent, task)
        sync_time = time.time() - sync_start

        # Async execution
        async_start = time.time()
        await orchestrator.execute_multiple_async(tasks)
        async_time = time.time() - async_start

        # Async should be significantly faster
        speedup = sync_time / async_time
        print(f"Speedup: {speedup:.2f}x")
        assert speedup > 2.0  # At least 2x faster for 3 tasks

#### Success Criteria

- ✅ All existing tests pass (backward compatibility)

- ✅ Async execution works for single agents

- ✅ Concurrent execution works for multiple agents

- ✅ 2-3x speedup for 3 concurrent tasks

- ✅ Performance metrics still tracked correctly

- ✅ CLI supports both sync and async modes

#### Effort Estimate

- Development: 8-12 hours

- Testing: 4-6 hours

- Documentation: 2-3 hours

- Total: 14-21 hours

### 1.2 Response Caching System

#### Objective

Implement intelligent caching of Claude API responses to reduce costs and latency for repeated queries.

#### Current State

Every identical prompt triggers a new API call, even if asked recently.

#### Target State

Intelligent caching with configurable TTL and cache invalidation.

#### Implementation Steps

Step 1: Create cache module

Create `claude_force/response_cache.py`:

    """
    Response caching system for Claude API calls.
    """
    import hashlib
    import json
    import time
    from pathlib import Path
    from typing import Optional, Dict, Any
    from dataclasses import dataclass, asdict
    from datetime import datetime, timedelta

    @dataclass
    class CacheEntry:
        """Cached response entry."""
        key: str
        agent_name: str
        task: str
        model: str
        response: str
        input_tokens: int
        output_tokens: int
        estimated_cost: float
        timestamp: float
        hit_count: int = 0

    class ResponseCache:
        """
        Intelligent response cache for Claude API calls.

        Features:
        - TTL-based expiration
        - LRU eviction
        - Size limits
        - Cache statistics
        - Exclusion lists (non-deterministic agents)
        """

        def __init__(
            self,
            cache_dir: Optional[Path] = None,
            ttl_hours: int = 24,
            max_size_mb: int = 100,
            enabled: bool = True
        ):
            self.cache_dir = cache_dir or Path.home() / ".claude" / "cache"
            self.cache_dir.mkdir(parents=True, exist_ok=True)

            self.ttl_seconds = ttl_hours * 3600
            self.max_size_bytes = max_size_mb * 1024 * 1024
            self.enabled = enabled

            # In-memory cache for fast access
            self._memory_cache: Dict[str, CacheEntry] = {}
            self._load_cache_index()

            # Statistics
            self.stats = {
                'hits': 0,
                'misses': 0,
                'evictions': 0,
                'size_bytes': 0
            }

        def _cache_key(self, agent_name: str, task: str, model: str) -> str:
            """Generate cache key."""
            content = f"{agent_name}:{task}:{model}"
            return hashlib.sha256(content.encode()).hexdigest()[:16]

        def get(
            self,
            agent_name: str,
            task: str,
            model: str
        ) -> Optional[Dict[str, Any]]:
            """Retrieve cached response."""
            if not self.enabled:
                return None

            key = self._cache_key(agent_name, task, model)

            # Check memory cache
            if key in self._memory_cache:
                entry = self._memory_cache[key]

                # Check TTL
                age = time.time() - entry.timestamp
                if age > self.ttl_seconds:
                    # Expired
                    self._evict(key)
                    self.stats['misses'] += 1
                    return None

                # Cache hit
                entry.hit_count += 1
                self.stats['hits'] += 1

                return {
                    'response': entry.response,
                    'input_tokens': entry.input_tokens,
                    'output_tokens': entry.output_tokens,
                    'estimated_cost': entry.estimated_cost,
                    'cached': True,
                    'cache_age_seconds': age
                }

            # Check disk cache
            cache_file = self.cache_dir / f"{key}.json"
            if cache_file.exists():
                # Check TTL
                age = time.time() - cache_file.stat().st_mtime
                if age > self.ttl_seconds:
                    cache_file.unlink()
                    self.stats['misses'] += 1
                    return None

                # Load from disk
                with open(cache_file) as f:
                    entry_dict = json.load(f)
                    entry = CacheEntry(**entry_dict)
                    self._memory_cache[key] = entry

                entry.hit_count += 1
                self.stats['hits'] += 1

                return {
                    'response': entry.response,
                    'input_tokens': entry.input_tokens,
                    'output_tokens': entry.output_tokens,
                    'estimated_cost': entry.estimated_cost,
                    'cached': True,
                    'cache_age_seconds': age
                }

            # Cache miss
            self.stats['misses'] += 1
            return None

        def set(
            self,
            agent_name: str,
            task: str,
            model: str,
            response: str,
            input_tokens: int,
            output_tokens: int,
            estimated_cost: float
        ):
            """Cache a response."""
            if not self.enabled:
                return

            key = self._cache_key(agent_name, task, model)

            entry = CacheEntry(
                key=key,
                agent_name=agent_name,
                task=task,
                model=model,
                response=response,
                input_tokens=input_tokens,
                output_tokens=output_tokens,
                estimated_cost=estimated_cost,
                timestamp=time.time()
            )

            # Store in memory
            self._memory_cache[key] = entry

            # Store on disk
            cache_file = self.cache_dir / f"{key}.json"
            with open(cache_file, 'w') as f:
                json.dump(asdict(entry), f)

            # Update size
            self.stats['size_bytes'] += cache_file.stat().st_size

            # Check size limit and evict if needed
            if self.stats['size_bytes'] > self.max_size_bytes:
                self._evict_lru()

        def _evict(self, key: str):
            """Evict specific cache entry."""
            if key in self._memory_cache:
                del self._memory_cache[key]

            cache_file = self.cache_dir / f"{key}.json"
            if cache_file.exists():
                size = cache_file.stat().st_size
                cache_file.unlink()
                self.stats['size_bytes'] -= size
                self.stats['evictions'] += 1

        def _evict_lru(self):
            """Evict least recently used entries."""
            # Sort by timestamp and hit_count
            entries = sorted(
                self._memory_cache.items(),
                key=lambda x: (x[1].hit_count, x[1].timestamp)
            )

            # Evict oldest 10%
            num_to_evict = max(1, len(entries) // 10)
            for key, _ in entries[:num_to_evict]:
                self._evict(key)

        def clear(self):
            """Clear entire cache."""
            for cache_file in self.cache_dir.glob("*.json"):
                cache_file.unlink()

            self._memory_cache.clear()
            self.stats['size_bytes'] = 0

        def get_stats(self) -> Dict[str, Any]:
            """Get cache statistics."""
            total_requests = self.stats['hits'] + self.stats['misses']
            hit_rate = (self.stats['hits'] / total_requests * 100) if total_requests > 0 else 0

            return {
                'enabled': self.enabled,
                'hits': self.stats['hits'],
                'misses': self.stats['misses'],
                'hit_rate': f"{hit_rate:.1f}%",
                'evictions': self.stats['evictions'],
                'size_mb': self.stats['size_bytes'] / (1024 * 1024),
                'entries': len(self._memory_cache)
            }

        def _load_cache_index(self):
            """Load cache index from disk."""
            for cache_file in self.cache_dir.glob("*.json"):
                try:
                    with open(cache_file) as f:
                        entry_dict = json.load(f)
                        entry = CacheEntry(**entry_dict)
                        self._memory_cache[entry.key] = entry
                        self.stats['size_bytes'] += cache_file.stat().st_size
                except Exception:
                    # Corrupted cache file, remove it
                    cache_file.unlink()

Step 2: Integrate cache with orchestrator

Update `claude_force/orchestrator.py`:

    class AgentOrchestrator:
        def __init__(self, ...):
            # Existing initialization
            self._response_cache = None

        @property
        def response_cache(self) -> ResponseCache:
            """Lazy-load response cache."""
            if self._response_cache is None:
                cache_config = self.config.get('cache', {})
                self._response_cache = ResponseCache(
                    enabled=cache_config.get('enabled', True),
                    ttl_hours=cache_config.get('ttl_hours', 24),
                    max_size_mb=cache_config.get('max_size_mb', 100)
                )
            return self._response_cache

        def execute_agent(self, agent_name: str, task: str, **kwargs) -> str:
            """Execute agent with caching support."""
            model = kwargs.get('model') or self._select_model(agent_name, task)

            # Check if agent should be excluded from caching
            exclude_list = self.config.get('cache', {}).get('exclude_agents', [])
            use_cache = agent_name not in exclude_list

            # Try cache first
            if use_cache:
                cached = self.response_cache.get(agent_name, task, model)
                if cached:
                    # Cache hit!
                    self._track_cached_execution(
                        agent_name=agent_name,
                        task=task,
                        cached_result=cached
                    )
                    return cached['response']

            # Cache miss - execute normally
            start_time = time.time()
            try:
                # Existing execution logic
                response = self.client.messages.create(...)
                result = response.content[0].text

                # Cache the response
                if use_cache:
                    self.response_cache.set(
                        agent_name=agent_name,
                        task=task,
                        model=response.model,
                        response=result,
                        input_tokens=response.usage.input_tokens,
                        output_tokens=response.usage.output_tokens,
                        estimated_cost=self._estimate_cost(...)
                    )

                # Track performance
                self._track_execution(...)

                return result

            except Exception as e:
                # Existing error handling
                raise

        def _track_cached_execution(self, agent_name: str, task: str, cached_result: dict):
            """Track cache hit in metrics."""
            self.performance_tracker.track_execution(
                agent_name=agent_name,
                task=task,
                success=True,
                execution_time_ms=1,  # Minimal time for cache hit
                model=cached_result.get('model', 'cached'),
                input_tokens=cached_result['input_tokens'],
                output_tokens=cached_result['output_tokens'],
                estimated_cost=0.0,  # No cost for cache hit
                metadata={'cached': True, 'cache_age': cached_result['cache_age_seconds']}
            )

Step 3: Add configuration

Update `.claude/claude.json`:

    {
      "cache": {
        "enabled": true,
        "ttl_hours": 24,
        "max_size_mb": 100,
        "exclude_agents": [
          "random-generator",
          "creative-writer"
        ]
      }
    }

Step 4: Add CLI commands for cache management

Update `claude_force/cli.py`:

    @click.group()
    def cache():
        """Manage response cache."""
        pass

    @cache.command()
    def stats():
        """Show cache statistics."""
        orchestrator = AgentOrchestrator()
        stats = orchestrator.response_cache.get_stats()

        click.echo("Cache Statistics")
        click.echo("=" * 40)
        click.echo(f"Enabled: {stats['enabled']}")
        click.echo(f"Hits: {stats['hits']}")
        click.echo(f"Misses: {stats['misses']}")
        click.echo(f"Hit Rate: {stats['hit_rate']}")
        click.echo(f"Evictions: {stats['evictions']}")
        click.echo(f"Size: {stats['size_mb']:.2f} MB")
        click.echo(f"Entries: {stats['entries']}")

    @cache.command()
    @click.confirmation_option(prompt='Are you sure you want to clear the cache?')
    def clear():
        """Clear response cache."""
        orchestrator = AgentOrchestrator()
        orchestrator.response_cache.clear()
        click.echo("✅ Cache cleared")

    cli.add_command(cache)

#### Testing Requirements

Unit Tests:

    # tests/test_response_cache.py
    def test_cache_hit():
        """Test cache hit returns cached response."""
        cache = ResponseCache()

        # First call - cache miss
        result1 = cache.get("python-expert", "Test task", "haiku")
        assert result1 is None

        # Set cache
        cache.set(
            "python-expert",
            "Test task",
            "haiku",
            "Test response",
            100,
            50,
            0.001
        )

        # Second call - cache hit
        result2 = cache.get("python-expert", "Test task", "haiku")
        assert result2 is not None
        assert result2['response'] == "Test response"
        assert result2['cached'] is True

    def test_cache_ttl_expiration():
        """Test cache entries expire after TTL."""
        cache = ResponseCache(ttl_hours=0.001)  # Very short TTL

        cache.set("agent", "task", "model", "response", 100, 50, 0.001)

        # Immediate retrieval - should hit
        result1 = cache.get("agent", "task", "model")
        assert result1 is not None

        # Wait for expiration
        import time
        time.sleep(5)  # 5 seconds > 0.001 hours

        # Should miss
        result2 = cache.get("agent", "task", "model")
        assert result2 is None

Integration Tests:

    # tests/integration/test_cache_integration.py
    def test_orchestrator_caching():
        """Test orchestrator uses cache correctly."""
        orchestrator = AgentOrchestrator()

        task = "Explain Python decorators"

        # First execution - no cache
        import time
        start1 = time.time()
        result1 = orchestrator.execute_agent("python-expert", task)
        time1 = time.time() - start1

        # Second execution - should use cache
        start2 = time.time()
        result2 = orchestrator.execute_agent("python-expert", task)
        time2 = time.time() - start2

        # Results should be identical
        assert result1 == result2

        # Second should be much faster (<100ms)
        assert time2 < 0.1  # Less than 100ms

        # Check cache stats
        stats = orchestrator.response_cache.get_stats()
        assert stats['hits'] >= 1

#### Success Criteria

- ✅ Cache correctly stores and retrieves responses

- ✅ TTL expiration works as expected

- ✅ Cache hit provides <100ms response time

- ✅ Excluded agents bypass cache

- ✅ Cache stats tracked accurately

- ✅ LRU eviction prevents unlimited growth

#### Effort Estimate

- Development: 10-14 hours

- Testing: 5-7 hours

- Documentation: 2-3 hours

- Total: 17-24 hours

### 1.3 Configuration & Documentation

#### Update Configuration Schema

Add to `.claude/claude.json`:

    {
      "performance": {
        "async_enabled": true,
        "max_concurrent_agents": 10
      },
      "cache": {
        "enabled": true,
        "ttl_hours": 24,
        "max_size_mb": 100,
        "exclude_agents": []
      }
    }

#### Update Documentation

1. Update `README.md` with new async capabilities

2. Add `docs/async-usage-guide.md`

3. Add `docs/caching-guide.md`

4. Update API documentation

#### Effort Estimate

- Documentation: 4-6 hours

## Phase 2: Advanced Optimizations

Duration: 3-4 weeks Priority: 🟡 MEDIUM-HIGH Expected Impact: 2-5x throughput improvement

### 2.1 Parallel Workflow Execution (DAG-based)

#### Objective

Enable parallel execution of independent workflow steps using Directed Acyclic Graph (DAG) scheduling.

#### Current State

    # Sequential execution
    for step in workflow_steps:
        result = execute_agent(step)
        context.append(result)

#### Target State

    # Parallel execution with dependency management
    async def execute_workflow_dag(workflow):
        dag = build_dependency_graph(workflow)
        while dag:
            # Execute all ready steps in parallel
            ready = get_steps_with_no_dependencies(dag)
            results = await asyncio.gather(*[execute_step(s) for s in ready])
            update_dag(dag, results)

#### Implementation Steps

Step 1: Add dependency tracking to workflows

Update workflow definitions in `.claude/claude.json`:

    {
      "workflows": {
        "code-quality-check": {
          "name": "Code Quality Check",
          "description": "Comprehensive code quality analysis",
          "steps": [
            {
              "id": "linter",
              "agent": "linter",
              "task": "Run Python linter",
              "dependencies": []
            },
            {
              "id": "type-checker",
              "agent": "type-checker",
              "task": "Run type checking",
              "dependencies": []
            },
            {
              "id": "security-scan",
              "agent": "security-scanner",
              "task": "Scan for security issues",
              "dependencies": []
            },
            {
              "id": "final-review",
              "agent": "code-reviewer",
              "task": "Final code review based on findings",
              "dependencies": ["linter", "type-checker", "security-scan"]
            }
          ]
        }
      }
    }

Step 2: Create DAG executor

Create `claude_force/workflow_dag.py`:

    """
    DAG-based workflow executor for parallel execution.
    """
    import asyncio
    from typing import Dict, List, Set, Any
    from dataclasses import dataclass
    from .async_orchestrator import AsyncAgentOrchestrator

    @dataclass
    class WorkflowStep:
        """Workflow step definition."""
        id: str
        agent: str
        task: str
        dependencies: List[str]
        result: Any = None
        completed: bool = False

    class WorkflowDAG:
        """DAG-based workflow executor."""

        def __init__(self, orchestrator: AsyncAgentOrchestrator):
            self.orchestrator = orchestrator

        def build_dag(self, workflow_config: dict) -> Dict[str, WorkflowStep]:
            """Build DAG from workflow configuration."""
            steps = {}

            for step_config in workflow_config['steps']:
                step = WorkflowStep(
                    id=step_config['id'],
                    agent=step_config['agent'],
                    task=step_config['task'],
                    dependencies=step_config.get('dependencies', [])
                )
                steps[step.id] = step

            # Validate no cycles
            self._validate_acyclic(steps)

            return steps

        def _validate_acyclic(self, steps: Dict[str, WorkflowStep]):
            """Validate DAG has no cycles."""
            def has_cycle(step_id: str, visited: Set[str], rec_stack: Set[str]) -> bool:
                visited.add(step_id)
                rec_stack.add(step_id)

                step = steps.get(step_id)
                if step:
                    for dep in step.dependencies:
                        if dep not in visited:
                            if has_cycle(dep, visited, rec_stack):
                                return True
                        elif dep in rec_stack:
                            return True

                rec_stack.remove(step_id)
                return False

            visited = set()
            rec_stack = set()

            for step_id in steps:
                if step_id not in visited:
                    if has_cycle(step_id, visited, rec_stack):
                        raise ValueError(f"Workflow contains cycle involving step: {step_id}")

        def get_ready_steps(self, steps: Dict[str, WorkflowStep]) -> List[WorkflowStep]:
            """Get steps with all dependencies satisfied."""
            ready = []

            for step in steps.values():
                if step.completed:
                    continue

                # Check if all dependencies are completed
                deps_completed = all(
                    steps[dep_id].completed
                    for dep_id in step.dependencies
                )

                if deps_completed:
                    ready.append(step)

            return ready

        async def execute_workflow(self, workflow_config: dict) -> Dict[str, Any]:
            """Execute workflow with parallel execution where possible."""
            import time

            start_time = time.time()
            steps = self.build_dag(workflow_config)

            results = {}
            execution_order = []

            print(f"Executing workflow: {workflow_config['name']}")
            print(f"Total steps: {len(steps)}")
            print()

            while any(not step.completed for step in steps.values()):
                # Get steps ready to execute
                ready = self.get_ready_steps(steps)

                if not ready:
                    # No steps ready but workflow not complete - error
                    incomplete = [s.id for s in steps.values() if not s.completed]
                    raise RuntimeError(f"Workflow deadlock. Incomplete steps: {incomplete}")

                print(f"Executing {len(ready)} step(s) in parallel: {[s.id for s in ready]}")

                # Execute ready steps in parallel
                step_start = time.time()
                step_results = await asyncio.gather(*[
                    self.orchestrator.execute_agent(step.agent, step.task)
                    for step in ready
                ])
                step_time = time.time() - step_start

                # Update results
                for step, result in zip(ready, step_results):
                    step.result = result
                    step.completed = True
                    results[step.id] = result
                    execution_order.append(step.id)

                print(f"  Completed in {step_time:.2f}s")
                print()

            total_time = time.time() - start_time

            return {
                'results': results,
                'execution_order': execution_order,
                'total_time_seconds': total_time,
                'workflow_name': workflow_config['name']
            }

Step 3: Integrate with orchestrator

Update `claude_force/orchestrator.py`:

    class AgentOrchestrator:
        async def execute_workflow_async(self, workflow_name: str) -> Dict[str, Any]:
            """Execute workflow with parallel execution."""
            workflow_config = self.config['workflows'].get(workflow_name)

            if not workflow_config:
                raise ValueError(f"Workflow '{workflow_name}' not found")

            from .workflow_dag import WorkflowDAG
            from .async_orchestrator import AsyncAgentOrchestrator

            async_orchestrator = AsyncAgentOrchestrator(
                config_path=self.config_path,
                api_key=self.api_key
            )

            dag = WorkflowDAG(async_orchestrator)
            return await dag.execute_workflow(workflow_config)

Step 4: Update CLI

Add workflow execution command:

    @click.command()
    @click.argument('workflow_name')
    @click.option('--parallel/--sequential', default=True, help='Use parallel execution')
    def run_workflow(workflow_name: str, parallel: bool):
        """Execute a workflow."""
        orchestrator = AgentOrchestrator()

        if parallel:
            result = asyncio.run(orchestrator.execute_workflow_async(workflow_name))
            click.echo(f"✅ Workflow completed in {result['total_time_seconds']:.2f}s")
            click.echo(f"Execution order: {' → '.join(result['execution_order'])}")
        else:
            # Sequential execution (existing)
            result = orchestrator.execute_workflow(workflow_name)
            click.echo("✅ Workflow completed")

#### Testing Requirements

    # tests/test_workflow_dag.py
    @pytest.mark.asyncio
    async def test_parallel_workflow_execution():
        """Test parallel workflow is faster than sequential."""
        orchestrator = AgentOrchestrator()

        # Workflow with 3 independent steps + 1 dependent
        workflow = {
            'name': 'Test Workflow',
            'steps': [
                {'id': 'step1', 'agent': 'python-expert', 'task': 'Task 1', 'dependencies': []},
                {'id': 'step2', 'agent': 'python-expert', 'task': 'Task 2', 'dependencies': []},
                {'id': 'step3', 'agent': 'python-expert', 'task': 'Task 3', 'dependencies': []},
                {'id': 'step4', 'agent': 'code-reviewer', 'task': 'Review all', 'dependencies': ['step1', 'step2', 'step3']}
            ]
        }

        import time
        start = time.time()
        result = await orchestrator.execute_workflow_async(workflow)
        parallel_time = time.time() - start

        # Should complete in ~2x single step time (not 4x)
        # Because first 3 steps run in parallel
        assert parallel_time < 10  # Assuming single step is ~3s
        assert len(result['results']) == 4

#### Success Criteria

- ✅ DAG correctly identifies dependencies

- ✅ Independent steps execute in parallel

- ✅ Dependent steps wait for prerequisites

- ✅ 2-3x speedup for workflows with parallelizable steps

- ✅ No deadlocks or race conditions

#### Effort Estimate

- Development: 12-16 hours

- Testing: 6-8 hours

- Documentation: 3-4 hours

- Total: 21-28 hours

### 2.2 Metrics Aggregation

Implementation: See Performance Analysis

#### Effort Estimate

- Development: 6-8 hours

- Testing: 3-4 hours

- Total: 9-12 hours

### 2.3 Query Result Caching

Implementation: Add LRU cache to AgentMemory queries

#### Effort Estimate

- Development: 4-6 hours

- Testing: 2-3 hours

- Total: 6-9 hours

## Phase 3: Polish & Enhancement

Duration: 2-3 weeks Priority: 🟢 LOW-MEDIUM Expected Impact: Improved reliability and monitoring

### 3.1 Enhanced Performance Monitoring

- Real-time performance dashboard

- Advanced analytics

- Anomaly detection

#### Effort Estimate

- Development: 8-12 hours

### 3.2 Circuit Breakers

- Fail-fast on API errors

- Automatic retry with exponential backoff

- Health checks

#### Effort Estimate

- Development: 6-8 hours

### 3.3 Advanced Caching Strategies

- Semantic caching (similar queries)

- Partial response caching

- Distributed cache support (Redis)

#### Effort Estimate

- Development: 10-14 hours

## Testing Strategy

### Unit Tests

Coverage Target: >90%

Key Areas:

- Async orchestrator

- Response cache (hit/miss/eviction)

- DAG builder (cycle detection)

- Workflow executor

### Integration Tests

Test Scenarios:

1. Full async workflow execution

2. Cache integration with real API

3. Parallel execution stress test

4. Performance regression tests

### Performance Tests

Benchmarks to Run:

1. Async vs sync comparison

2. Cache hit rate measurement

3. Parallel speedup verification

4. Resource usage profiling

### Regression Tests

Automated Checks:

- Performance baselines

- API compatibility

- Backward compatibility

## Migration & Backward Compatibility

### Backward Compatibility Strategy

100% Backward Compatible

All new features are opt-in:

    # Old code continues to work unchanged
    orchestrator = AgentOrchestrator()
    result = orchestrator.execute_agent("python-expert", "task")

    # New async API is optional
    result = await orchestrator.execute_agent_async("python-expert", "task")

### Migration Path

Phase 1: Soft Launch

- New features available but not default

- Documentation and examples provided

- Monitoring of adoption

Phase 2: Encourage Adoption

- Performance benefits highlighted

- CLI defaults to async (with –sync option)

- Cache enabled by default

Phase 3: Full Adoption

- Deprecation warnings for sync-only usage

- Async becomes default in v3.0

### Configuration Migration

Auto-migration on first run:

    def migrate_config(config: dict) -> dict:
        """Auto-migrate config to new schema."""
        if 'cache' not in config:
            config['cache'] = {
                'enabled': True,
                'ttl_hours': 24,
                'max_size_mb': 100
            }

        if 'performance' not in config:
            config['performance'] = {
                'async_enabled': True,
                'max_concurrent_agents': 10
            }

        return config

## Risk Assessment

### High-Risk Areas

#### 1. Async Implementation Complexity

Risk: Async/await introduces complexity, potential for deadlocks

Mitigation:

- Comprehensive testing with asyncio test framework

- Timeout mechanisms on all async operations

- Backward compatible sync API maintained

- Code review by async-experienced developers

Contingency: Rollback to sync if critical issues found

#### 2. Cache Correctness

Risk: Stale cache responses, incorrect behavior

Mitigation:

- Conservative TTL defaults (24 hours)

- Exclude non-deterministic agents by default

- Cache invalidation on config changes

- Clear cache command readily available

- Extensive testing of cache behavior

Contingency: Disable cache by default if issues arise

#### 3. DAG Complexity

Risk: Workflow execution errors, deadlocks

Mitigation:

- Cycle detection in DAG validation

- Timeout on workflow execution

- Detailed logging of execution order

- Fallback to sequential execution on errors

Contingency: Sequential execution remains available

### Medium-Risk Areas

#### 4. Performance Regression

Risk: Optimization actually slows things down

Mitigation:

- Comprehensive benchmarking before/after

- Automated performance regression tests

- A/B testing in staging environment

Contingency: Feature flags to disable optimizations

#### 5. Increased Memory Usage

Risk: Caching and async may increase memory

Mitigation:

- Cache size limits enforced

- LRU eviction implemented

- Memory profiling during development

- Monitoring in production

Contingency: Configurable cache limits, can be disabled

## Success Metrics

### Performance Metrics

Metric | Current | Target | Measurement
--- | --- | --- | ---
Workflow Time (3 agents) | 12-30s | 4-10s | Benchmarks
Cache Hit Rate | 0% | 20-70% | Cache stats
Cost Per Execution | Baseline | -30-50% | Analytics
Throughput | 1x | 2-5x | Load tests
P95 Latency | 10s | 5s | Metrics

### Quality Metrics

Metric | Target | Measurement
--- | --- | ---
Test Coverage | >90% | pytest-cov
Success Rate | >95% | Analytics
Backward Compatibility | 100% | Integration tests
Documentation Coverage | 100% | Manual review

### Adoption Metrics

Metric | Target (3 months) | Measurement
--- | --- | ---
Async API Usage | >50% | Telemetry
Cache Enabled | >80% | Config analysis
Parallel Workflows | >30% | Usage stats

## Implementation Timeline

### Month 1: Phase 1 - Foundation

Week 1-2: Async Implementation

- Create AsyncAgentOrchestrator

- Add async methods to AgentOrchestrator

- Update CLI with async support

- Write unit tests

- Write integration tests

Week 3-4: Response Caching

- Create ResponseCache module

- Integrate with orchestrator

- Add cache CLI commands

- Test cache correctness

- Performance benchmarks

Deliverables:

- ✅ Async API working

- ✅ Response caching functional

- ✅ 90%+ test coverage

- ✅ Documentation updated

### Month 2: Phase 2 - Advanced Optimization

Week 1-2: Parallel Workflows

- Create WorkflowDAG module

- Update workflow schema

- Implement DAG executor

- Add workflow CLI commands

- Test parallel execution

Week 3: Metrics & Caching

- Implement metrics aggregation

- Add query result caching

- Performance testing

Week 4: Integration & Testing

- End-to-end integration tests

- Performance regression tests

- Load testing

- Bug fixes

Deliverables:

- ✅ Parallel workflows working

- ✅ 2-5x speedup demonstrated

- ✅ All tests passing

### Month 3: Phase 3 - Polish & Release

Week 1-2: Enhancements

- Enhanced monitoring

- Circuit breakers

- Advanced caching

Week 3: Documentation & Examples

- Usage guides

- API documentation

- Example workflows

- Migration guide

Week 4: Release Preparation

- Final testing

- Performance validation

- Release notes

- Version 2.3.0 release

Deliverables:

- ✅ Production-ready release

- ✅ Complete documentation

- ✅ Performance targets met

## Next Steps

### Immediate Actions

1. Review & Approve Plan - Stakeholder review (1 week)

2. Set Up Development Environment - Create feature branch

3. Begin Phase 1 - Start async implementation

### Decision Points

Week 2: Review async implementation, decide to proceed with caching

Week 6: Review Phase 1 results, decide to proceed with Phase 2

Week 10: Review overall progress, decide on Phase 3 scope

### Success Gates

Each phase requires:

- ✅ All tests passing

- ✅ Performance targets met

- ✅ Documentation complete

- ✅ Code review approved

## Appendix

### A. Code Review Checklist

- Async operations have timeouts

- Error handling for all async code

- Cache eviction prevents memory leaks

- DAG validation prevents cycles

- Backward compatibility maintained

- Tests cover edge cases

- Documentation updated

- Performance benchmarks run

### B. Performance Testing Script

    #!/bin/bash
    # performance_validation.sh

    echo "Running performance validation..."

    # Baseline (sync)
    python benchmark_sync.py > baseline.txt

    # With async
    python benchmark_async.py > async.txt

    # With caching
    python benchmark_with_cache.py > cache.txt

    # With parallel workflows
    python benchmark_parallel_workflows.py > parallel.txt

    # Compare results
    python compare_results.py baseline.txt async.txt cache.txt parallel.txt

### C. Rollback Plan

If critical issues found:

1. Immediate: Disable feature via config flag

2. Short-term: Revert to previous version

3. Long-term: Fix issues and re-release

Feature flags for easy rollback:

    {
      "features": {
        "async_enabled": false,
        "cache_enabled": false,
        "parallel_workflows_enabled": false
      }
    }

Document Version: 1.0 Last Updated: 2025-11-14 Next Review: After Phase 1 completion Owner: Performance Engineering Team