Implement Base Agent Framework for Agentic AI

[manavgup]

Implement Base Agent Framework

Priority: HIGH

Phase: Agentic AI Foundation (Sprint 8, Weeks 1-2)
Transforms: RAG system into autonomous agent system

Core Components to Build

1. Agent Base Architecture

• Design BaseAgent abstract class
• Implement agent state management
• Create agent registry and factory
• Build agent configuration system
• Implement agent lifecycle management
• Create inter-agent communication protocol
• Build agent monitoring and logging
• Implement error handling and recovery

2. Planning & Reasoning System

• Implement Chain-of-Thought (CoT) reasoning
• Create ReAct (Reasoning + Acting) framework
• Build task decomposition system
• Implement goal-oriented planning
• Create plan validation system
• Build plan execution engine
• Implement plan monitoring
• Create adaptive planning

3. Memory Management

• Design short-term memory for conversations
• Implement long-term memory with vector storage
• Create episodic memory for task sequences
• Build semantic memory for knowledge
• Implement memory retrieval system
• Create memory consolidation
• Build memory pruning strategy
• Implement persistence layer

Implementation Structure

# backend/rag_solution/agents/base_agent.py
class BaseAgent(ABC):
    def __init__(self, config: AgentConfig):
        self.planner = Planner()
        self.executor = Executor()
        self.memory = MemoryManager()
        self.reflector = Reflector()
    
    @abstractmethod
    async def run(self, task: Task) -> Result:
        # 1. Plan
        plan = await self.planner.create_plan(task)
        
        # 2. Execute
        results = await self.executor.execute(plan)
        
        # 3. Reflect
        evaluation = await self.reflector.evaluate(results)
        
        # 4. Store
        await self.memory.store(task, plan, results)
        
        return results

# backend/rag_solution/agents/planner.py
class Planner:
    def create_plan(self, task: Task) -> Plan:
        # Decompose task
        # Select strategy
        # Create step sequence
        # Validate feasibility

# backend/rag_solution/agents/memory.py
class MemoryManager:
    def __init__(self):
        self.working_memory = {}  # Current context
        self.episodic_memory = []  # Past interactions
        self.semantic_memory = VectorStore()  # Knowledge

Configuration Schema

agent:
  type: "base"
  planning:
    strategy: "chain_of_thought"
    max_depth: 5
    timeout: 60
  memory:
    working_size: 100
    episodic_retention: "7d"
    semantic_threshold: 0.8
  execution:
    max_retries: 3
    error_strategy: "fallback"

Acceptance Criteria

• Base agent class implemented and tested
• Planning system can decompose complex tasks
• Memory system persists across sessions
• Agent can self-reflect on performance
• Error recovery mechanisms work
• Configuration system is flexible
• Monitoring provides insights
• Documentation complete

Dependencies

• Existing RAG infrastructure
• LLM providers (OpenAI, Anthropic, WatsonX)
• Vector store for memory
• Message queue for agent communication

Effort: 2 weeks
Team: Backend development
Next: Tool integration framework

[manavgup]

Superseded by design docs (docs/design/agent-mcp-architecture.md). Agent framework design completed Nov 2025. Implementation deferred to Phase 4.