MediAgent - LangGraph & LangSmith Implementation of the Microsoft Medical Agent

Multi-Agent Medical DiagnosisAI Framework ImplementationDebugging and ObservabilityCode Review Best PracticesLangChainLangGraphLangSmithGitHubMicrosoft Teamsaimulti-agent-systemsmedical-ailangchainlanggraphlangsmithsoftware-developmentdebugging

July 7, 2025(about 20 days ago)55:18

Key Takeaways

Business

•Multi-agent orchestration can handle complex, non-deterministic medical diagnosis tasks effectively.
•Integrating tools like Microsoft Teams and GitHub facilitates collaboration and version control in AI development projects.
•Applying structured frameworks improves workflow and encourages iterative feature development.

Technical

•The paper demonstrates the power of Chain of Debate orchestration in AI reasoning.
•LangChain, LangGraph, and LangSmith enable recreation and tracing of multi-agent workflows.
•Dynamic abstraction layers and feature branch workflows help manage complex codebases and debugging.

Personal

•Code reviews are critical for catching errors and ensuring high-quality AI implementations.
•Engaging in code walkthrough sessions deepens understanding of complex systems.
•Debugging catastrophic errors requires persistence and a systematic approach to problem-solving.

In this episode of The Build, Cameron Rohn and Tom Spencer dive into the development and architecture behind MediAgent, a cutting-edge AI medical agent built with LangGraph and LangSmith. They begin by unpacking the integration of Microsoft’s Medical Agent APIs within a robust technical framework that leverages LangSmith’s tooling to enhance AI workflow observability and debugging. This combination allows for precise tracking of the agent’s decision-making processes, crucial for maintaining high accuracy in sensitive healthcare scenarios. The conversation then shifts to the importance of building in public, with Cameron and Tom sharing their experiences using platforms like Vercel and Supabase to rapidly deploy and iterate on MediAgent. They emphasize how transparent development cycles not only foster community trust but also drive faster feedback loops, essential for refining AI-driven products. They explore critical architecture decisions, including how MCP tools facilitate modular code management and seamless API integration, enabling scalable and maintainable infrastructures. The discussion touches on entrepreneurship insights, highlighting monetization strategies relevant to AI startups navigating regulatory complexities and market adoption challenges. Closing with a forward-looking perspective, Cameron and Tom underscore the growing potential of AI agents in transforming healthcare delivery. They encourage developers and entrepreneurs to embrace open-source collaboration and iterative public building as keys to unlocking innovation in AI-driven industries.