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Pro Track5.5 Hours•Level: Intermediate•Updated: October 2026

Autonomous Reasoning Agents & Model Context Protocol (MCP)

Move past toy agent frameworks. Design production multi-agent swarms using LangGraph, deterministic state persistence, sandboxed execution, and Anthropic's Model Context Protocol (MCP).

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Curriculum Outcomes & Engineering Deliverables

Implement resilient ReAct feedback loops with tool call schema validation
Construct cyclic state graphs with checkpointing and human-in-the-loop approvals
Build production Model Context Protocol (MCP) servers in Python and TypeScript
Orchestrate hierarchical multi-agent teams with shared short and long-term memory

Included Engineering Deliverables

Production Model Context Protocol (MCP) server in FastMCP
Cyclic LangGraph state machine with time-travel rewind
Sandboxed Docker tool execution engine with syscall whitelisting
Hierarchical Supervisor-Specialist agent team orchestrator

Curriculum Modules

3 Modules • 5 Lessons
Module 012 Lessons

Cognitive Architectures & ReAct Loops

Grounding LLMs in reality through structured reasoning and tool execution.

Module 022 Lessons

Model Context Protocol (MCP)

The universal open standard connecting AI models to external tools and data.

Module 031 Lessons

Autonomous Multi-Agent Swarms

Coordinating specialist agents with hierarchical supervisory networks.

Hardware & GPU Compute

Baseline GPUNot GPU dependent (API/Ollama hybrid)
VRAM Requirement8GB System RAM minimum
Recommended ConfigurationLocal Ollama server + Claude 3.5/3.7 API keys

Prerequisites & Tech Stack

Stack Components
LangGraphFastMCPAnthropic Claude 3.7SQLite CheckpointerDocker SDK
Knowledge Prerequisites
  • Python async/await
  • Basic REST/JSON-RPC
  • LLM Foundations