Most AI Agent frameworks treat the model as a black box: you register tools, the model picks one, the tool runs, and the cycle repeats. This pattern is perfect for demos, but for a production system, it requires more complex systems. We need to manage context windows, cache API calls, filter sensitive tools by role, and compact the information history within models to avoid token limits. I landed on middleware while reviewing issues for deepagents and understanding their codebase. This is when I started to wonder what middleware really is in the context of AI agents and its significance. This got me thinking: how do other frameworks handle this problem? So I went ahead and installed Pydantic AI, read the CrewAI source, and checked Langchain and Autogen.