标签：#deepagent

If you've built even a simple AI agent, you've probably noticed that the "agent loop" itself is deceptively simple: the model gets a message, decides whether to call a tool, gets the result back, and repeats until it has an answer. But real-world agents need a lot more than that bare loop to actually work well. What happens when a conversation gets so long it blows past the model's context window? What if a tool call gets interrupted halfway through and leaves your message history in a broken state? What if you want the agent to keep a running todo list of what it's working on, or delegate parts of a task to a specialized sub-agent, or read and write files as part of its job? You could bolt all of this onto your agent manually. Or, if you're using Deep Agents, you get most of it for free through something called middleware . This post walks through what middleware actually is, why Deep Agents ships with a default stack of it, and how each piece behaves, with runnable code for each one so you can see it working instead of just reading about it. So What Is Middleware, Really? If you've done any web development, the term "middleware" probably already rings a bell. It's the same idea here. Middleware is code that sits around the core agent loop and gets a chance to run before or after certain things happen, like before a tool call executes, after the model responds, or right before messages are sent to the model. Instead of writing all of this logic directly inside your agent, you attach separate, independent pieces of middleware that each handle one specific concern. This matters for two reasons: You don't have to build common behaviors from scratch. Things like managing a todo list, summarizing long conversations, or handling file access are problems almost every non-trivial agent runs into. Deep Agents ships default middleware for these so you don't reinvent them every time. You can customize behavior without touching the agent's core logic. Need a custom summarizati