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Model Context Protocol (MCP) promises to simplify how AI Agents interact with tools. But what actually changes when you use it in a real system? In this video, I take an existing multi-agent security application built with Red, Blue, and Judge agents, and refactor it using FastMCP in Python.
You've built an Orchestrator that can plan/replan its own steps. That’s powerful, but it can't judge its own work. If it generates broken code or misses a key detail, it just hands that bad first draft straight to your user. In this video, we're looking at the Evaluator Loop.
Orchestrator == LLMs That Plan Themselves If your AI workflow breaks the moment a task becomes unpredictable, you are stuck in a pipeline. Here is how to fix it by moving some of your your control flow out of your code and into the LLM. In this masterclass, we break down the Orchestrator Pattern.
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