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Learn to build real-time AI agent systems using the Multi-Context Protocol (MCP) through hands-on development of a multiplayer game where AI agents make decisions and communicate in structured ways. Explore how MCP enables modular agent behavior by splitting functionality into distinct contexts like planning, reasoning, and execution, then connecting them using Python, CrewAI, and FastAPI. Discover the power of hot-swappable LLMs by seamlessly switching between GPT-4, Claude, and Mixtral models during runtime to observe performance differences without modifying core code. Master the implementation of live A/B testing across agents by swapping models mid-game while capturing structured MCP metrics including planning time, replanning frequency, and context-switching impact on outcomes. Gain insights into benchmarking LLM performance beyond traditional metrics like token count and latency to optimize your architecture effectively. Work with an open source repository to understand the underlying architecture and see how this protocol-based design approach can enhance any multi-agent system beyond gaming applications, providing a clean alternative to complex orchestration patterns.
