PRD 02: Agent Factory & Lifecycle Management

1. Overview

Purpose

The Agent Factory creates intelligent, specialized agents with real capabilities, not just database records. Each agent is a "cell" in the Context Engineering paradigm, with its own memory, skills, and tools.

Vision Alignment

• Agents are living entities with LLM connections

• Each agent has specialized capabilities via fine-tuning/prompting

• Agents maintain cellular memory across interactions

• Agents can evolve based on performance

2. Problem Statement

Current agents are just database entries with no:

• Actual LLM connections

• Real skill implementation

• Tool execution capabilities

• Memory persistence

• Performance tracking

• Learning mechanisms

3. Success Criteria

• Agents execute real tasks via LLM

• Skills translate to actual capabilities

• Tools (MCP servers) are accessible

• Memory persists across sessions

• Performance improves over time

4. Functional Requirements

4.1 Agent Creation & Configuration

class AgentFactory:
    """
    Creates fully-functional AI agents with real capabilities
    """
    
    async def create_agent(
        self,
        name: str,
        agent_type: str,
        model_config: ModelConfig,
        skills: List[Skill],
        tools: List[MCPTool],
        memory_config: MemoryConfig
    ) -> Agent:
        # Create LLM connection
        # Configure base prompting
        # Attach skill-based contexts
        # Connect MCP tools
        # Initialize memory system
        # Set up performance tracking

4.2 Agent Lifecycle States

class AgentLifecycle(Enum):
    INITIALIZING = "initializing"    # Being created
    TRAINING = "training"            # Learning phase
    ACTIVE = "active"               # Ready for tasks
    BUSY = "busy"                   # Executing task
    LEARNING = "learning"           # Updating from feedback
    HIBERNATING = "hibernating"     # Suspended to save resources
    RETIRED = "retired"             # No longer active

4.3 Skill Implementation

class SkillManager:
    """
    Translates skills into actual agent capabilities
    """
    
    async def apply_skill(
        self,
        agent: Agent,
        skill: Skill
    ) -> EnhancedAgent:
        # Add skill-specific prompting
        # Include domain knowledge
        # Add example patterns
        # Configure skill parameters
        
    def get_skill_prompt_enhancement(self, skill: Skill) -> str:
        """
        Returns prompt additions for specific skills
        """
        skill_prompts = {
            "code_analysis": "You are an expert code reviewer...",
            "data_processing": "You excel at data transformation...",
            "security_audit": "You are a security specialist...",
            "api_design": "You are an API architect..."
        }

4.4 Tool Integration (MCP)

class ToolConnector:
    """
    Connects agents to MCP servers and tools
    """
    
    async def attach_tools(
        self,
        agent: Agent,
        tools: List[MCPTool]
    ) -> ToolEnabledAgent:
        # Connect to MCP servers
        # Register tool capabilities
        # Create tool execution interface
        # Set up tool result handling
        
    async def execute_tool(
        self,
        agent: Agent,
        tool_name: str,
        parameters: Dict
    ) -> ToolResult:
        # Validate tool access
        # Execute via MCP bridge
        # Parse results
        # Update agent context

5. Technical Architecture

5.1 Agent Architecture

┌─────────────────────────────────────────────────────────┐
│                        Agent                            │
├─────────────────────────────────────────────────────────┤
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │ LLM Provider │  │ Skill Engine │  │ Tool Manager │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐  │
│  │    Memory    │  │   Context    │  │  Performance │  │
│  └──────────────┘  └──────────────┘  └──────────────┘  │
└─────────────────────────────────────────────────────────┘

5.2 Agent Types & Specializations

class AgentTypes:
    CODE_ARCHITECT = {
        "base_model": "gpt-4",
        "temperature": 0.7,
        "system_prompt": "Expert software architect...",
        "required_skills": ["system_design", "code_review", "api_design"],
        "recommended_tools": ["github", "docker", "kubernetes"]
    }
    
    DATA_ANALYST = {
        "base_model": "gpt-4",
        "temperature": 0.3,
        "system_prompt": "Data analysis specialist...",
        "required_skills": ["data_processing", "statistics", "visualization"],
        "recommended_tools": ["pandas", "jupyter", "sql"]
    }
    
    SECURITY_EXPERT = {
        "base_model": "gpt-4",
        "temperature": 0.2,
        "system_prompt": "Cybersecurity expert...",
        "required_skills": ["security_audit", "penetration_testing", "compliance"],
        "recommended_tools": ["owasp", "metasploit", "nmap"]
    }

6. Implementation Details

6.1 Agent Initialization Flow

async def initialize_agent(self, config: AgentConfig) -> Agent:
    # Step 1: Create base agent in database
    db_agent = await self.create_db_agent(config)
    
    # Step 2: Initialize LLM connection
    llm_provider = LLMProvider(
        provider=config.model_provider,
        model=config.model_name,
        temperature=config.temperature,
        api_key=config.api_key
    )
    
    # Step 3: Build system prompt with skills
    system_prompt = self.build_system_prompt(
        base_prompt=config.base_prompt,
        skills=config.skills,
        personality=config.personality
    )
    
    # Step 4: Initialize memory system
    memory = MemorySystem(
        agent_id=db_agent.id,
        memory_type=config.memory_type,
        retention_policy=config.retention_policy
    )
    
    # Step 5: Connect tools via MCP
    tools = await self.mcp_bridge.connect_tools(
        agent_id=db_agent.id,
        tool_configs=config.tools
    )
    
    # Step 6: Create agent runtime
    agent_runtime = AgentRuntime(
        db_agent=db_agent,
        llm=llm_provider,
        system_prompt=system_prompt,
        memory=memory,
        tools=tools
    )
    
    # Step 7: Run initialization tests
    await self.test_agent_capabilities(agent_runtime)
    
    return agent_runtime

6.2 Agent Execution Pipeline

async def execute_task(self, agent: Agent, task: Task) -> TaskResult:
    # Phase 1: Context Preparation
    context = await self.prepare_context(
        task=task,
        agent_memory=agent.memory,
        global_context=self.orchestrator_context
    )
    
    # Phase 2: Prompt Generation (Context Engineering)
    prompt = await self.prompt_orchestrator.generate(
        task=task,
        agent=agent,
        context=context,
        strategy="molecular"  # atoms + examples + context
    )
    
    # Phase 3: LLM Execution
    response = await agent.llm.generate(prompt)
    
    # Phase 4: Tool Execution (if needed)
    if response.requires_tools:
        tool_results = await agent.execute_tools(response.tool_calls)
        response = await agent.llm.generate_with_tools(
            prompt,
            tool_results
        )
    
    # Phase 5: Memory Update
    await agent.memory.store(
        task=task,
        response=response,
        performance_metrics=self.calculate_metrics(response)
    )
    
    # Phase 6: Learning Feedback
    await self.learning_engine.process_feedback(
        agent=agent,
        task=task,
        result=response
    )
    
    return response

7. Database Schema Updates

-- Extend agents table
ALTER TABLE agents ADD COLUMN llm_config JSONB;
ALTER TABLE agents ADD COLUMN system_prompt TEXT;
ALTER TABLE agents ADD COLUMN execution_stats JSONB;
ALTER TABLE agents ADD COLUMN learning_state JSONB;

-- Agent runtime configuration
CREATE TABLE agent_runtimes (
    id SERIAL PRIMARY KEY,
    agent_id INTEGER REFERENCES agents(id),
    llm_provider VARCHAR(50),
    model_name VARCHAR(100),
    temperature FLOAT,
    max_tokens INTEGER,
    context_window INTEGER,
    api_key_ref VARCHAR(255), -- Reference to secure storage
    created_at TIMESTAMP DEFAULT NOW()
);

-- Agent-tool relationships
CREATE TABLE agent_tools (
    id SERIAL PRIMARY KEY,
    agent_id INTEGER REFERENCES agents(id),
    tool_id INTEGER REFERENCES tools(id),
    configuration JSONB,
    access_level VARCHAR(50),
    usage_count INTEGER DEFAULT 0,
    last_used TIMESTAMP
);

-- Agent performance tracking
CREATE TABLE agent_performance (
    id SERIAL PRIMARY KEY,
    agent_id INTEGER REFERENCES agents(id),
    task_id INTEGER REFERENCES tasks(id),
    execution_time FLOAT,
    token_usage JSONB,
    quality_score FLOAT,
    error_count INTEGER,
    success BOOLEAN,
    recorded_at TIMESTAMP DEFAULT NOW()
);

8. API Endpoints

# Create specialized agent
POST /api/agents/create-specialized
{
    "name": "Code Review Expert",
    "type": "code_architect",
    "model": {
        "provider": "openai",
        "name": "gpt-4",
        "temperature": 0.7
    },
    "skills": ["code_review", "security_audit", "performance_analysis"],
    "tools": ["github", "sonarqube", "datadog"],
    "memory": {
        "type": "hierarchical",
        "retention_days": 30
    }
}

# Execute agent task
POST /api/agents/{agent_id}/execute
{
    "task": {
        "description": "Review this Python code for security issues",
        "code": "...",
        "context": {...}
    },
    "execution_mode": "thorough",
    "use_tools": true
}

# Update agent learning
POST /api/agents/{agent_id}/learn
{
    "feedback": {
        "task_id": "...",
        "quality_score": 8.5,
        "corrections": [...],
        "improvements": [...]
    }
}

# Get agent performance
GET /api/agents/{agent_id}/performance?period=7d

9. Integration with Existing Code

Use Existing Services

# In agent_factory.py
from services.llm_provider import LLMProvider, LLMConfig
from services.memory_service import MemorySystemService
from services.mcp_bridge import EnhancedMCPBridge
from context_engineering.prompt_builder import ContextAwarePromptBuilder

class EnhancedAgentFactory:
    def __init__(self):
        self.llm_provider = LLMProvider()
        self.memory_service = MemorySystemService()
        self.mcp_bridge = EnhancedMCPBridge()
        self.prompt_builder = ContextAwarePromptBuilder()

10. Testing Strategy

Unit Tests

• Agent creation with all configurations

• Skill application to prompts

• Tool execution via MCP

• Memory persistence

Integration Tests

• End-to-end task execution

• Multi-tool workflows

• Learning feedback loop

• Performance tracking

Acceptance Criteria

• Agent executes real LLM calls

• Tools produce actual results

• Memory persists between sessions

• Performance improves with learning

11. Dependencies

• Services: llm_provider.py, memory_service.py, mcp_bridge.py

• PRD 01: Orchestration Engine (for task assignment)

• PRD 03: Context Engineering (for prompt optimization)

• PRD 05: Memory Systems (for persistence)

12. Timeline

• Week 1: Agent runtime creation

• Week 2: LLM integration

• Week 3: Tool connectivity

• Week 4: Learning mechanisms

13. Success Metrics

• Agent creation success rate: 100%

• Task execution success rate: > 90%

• Tool execution reliability: > 95%

• Memory retrieval accuracy: > 90%

• Performance improvement over time: > 20%