AI Agent Orchestrator Guide

This skill provides the architectural framework for building robust, autonomous, and multi-agent AI systems.

🤖 Agent Design & Logic

• Single vs Multi-Agent: Choose Single Agent for linear tasks and Multi-Agent (orchestrated via LangGraph or Swarm) for complex, parallelizable workflows.
• Reasoning Patterns: Use ReAct (Reason + Act) for standard tool usage. Leverage Chain-of-Thought (CoT) for complex reasoning before action.
• MCP Integration: Use Model Context Protocol (MCP) to provide agents with secure, standardized access to local and remote tools/data.

🧠 Memory & Context Management

• Persistence: Use Checkpointers (LangGraph) to persist state across sessions and enable "Time Travel" (debugging/rewinding state).
• Short-term vs Long-term: Manage ephemeral session state separately from durable user/project memory.
• Context Window: Prune or summarize message history to stay within token limits while maintaining essential context.

🔗 LangChain & LangGraph

• Chains: Use LCEL (LangChain Expression Language) for composing modular, readable agent components.
• Graphs: Define state machines using LangGraph for non-linear, cyclic agent workflows.
• Human-in-the-Loop: Implement interrupt() points for critical decisions requiring user approval (e.g., payments, file deletions).

🛠 Tool Usage & Security

• Tool Definition: Tools must have clear, descriptive names and JSON schemas.
• Validation: Sanitize tool inputs using Pydantic or Zod.
• Security: Never give agents access to root shell or unrestricted network calls without strict sandboxing and human oversight.

🧪 Testing & Observability

• Evaluation: Use LangSmith or similar tools to trace agent runs and evaluate performance against benchmarks.
• Logging: Log all tool calls, agent thoughts, and state transitions for auditability.

🔗 References

• LangGraph Documentation
• LangChain Documentation
• Swarm (OpenAI) Patterns