AI Autonomous Agents, Tool Calling & MCP (LangGraph) › 10. CrewAI vs AutoGen vs LangGraph: Architectural Benchmarks for Enterprise Production

Course Syllabus

01. What are Autonomous AI Agents? The Shift from Passive Chatbots to Action-Taking Systems 02. Cognitive Architectures: ReAct (Reason + Act), Plan-and-Solve & Reflexion Memory Loops 03. Function Calling & Tool Execution: JSON Schema Contracts, Dynamic Dispatch & Error Recovery

04. LangGraph Core: Why DAGs Fail and Why Cyclical Graph Architecture is Mandatory for Agents 05. StateGraph Architecture: TypedDict State Schemas, State Reducers & Channel Updates 06. Conditional Edges: Routing Agent Actions dynamically based on LLM decisions 07. Persistence & Memory: SQLite / PostgreSQL Checkpointers for Resumable Agent Conversations

08. Multi-Agent Systems (MAS): The Supervisor Pattern (Central Coordinator Orchestrating Specialized Agents) 09. Peer-to-Peer Multi-Agent Communication & Consensus Protocols (Researcher, Coder, Critic, Reviewer) 10. CrewAI vs AutoGen vs LangGraph: Architectural Benchmarks for Enterprise Production

11. Model Context Protocol (MCP) Deep Dive: The Universal Open Standard for AI Tool Integration (Anthropic Spec) 12. MCP Architecture: Transports (stdio vs SSE), Resources, Prompts & Tool Declarations 13. Building Custom MCP Servers from Scratch in TypeScript / Node.js and Python 14. Connecting MCP Servers to Antigravity, Claude Desktop & LangGraph AI Pipelines

15. Human-in-the-Loop (HITL): Interrupts, Approval Gates & Manual State Override before Tool Execution 16. Time-Travel Debugging: Rewinding Agent State, Modifying Memory & Forking Execution Branches 17. Sandboxed Tool Execution: Running Untrusted Code safely in Docker Containers & E2B Sandboxes 18. Production Case Study: Building an Autonomous Full-Stack Software Engineer AI Agent from Scratch