In Parts 1 through 4 we built a useful tool: a USC campus assistant that knows when to retrieve, when to refuse, and which endpoint to call. It is still a chatbot. The model writes a string; we print it. Everything interesting happened inside one model call.
This post turns it into an agent. By agent I mean something specific and small — the model can choose a tool from a list, your Python code runs that tool, and the result goes back into the conversation. That's it. No LangGraph, no AutoGen, no LangChain. Two functions, one loop, and a NIM call with tools=....
You'll watch the model decide for itself whether to consult the clock, search the USC knowledge base, or just answer directly. Once you see the loop, the framework abstractions on top of it are easier to read because you already know what they hide.
I'm B Torkian, NVIDIA Developer Champion at USC. Final part of the series.
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