"Explain how tool use works in LLMs and describe the agent loop."

The moment you move from "I've used ChatGPT" to "I build AI systems," this is the question that draws the line. Deliver it in 60–90 seconds without reciting.

Miss one and they probe.

1. Who executes — the LLM requests, your code runs it, never the reverse
2. The loop cycle — observe → reason → act (tool call) → execute → inject result → observe again
3. What makes a good tool definition — name, description (the field that matters most), typed schema
4. Failure modes + one fix each — infinite loop, hallucinated tool call, tool output poisoning
5. When NOT to loop — a single prompt with sufficient context beats a loop every time

"Tool use is the mechanism where an LLM produces a structured call — a tool name and arguments — and the host application decides whether to execute it. The model never runs code directly. An agent loop wraps this in a cycle: observe the context, reason, emit a tool call, inject the result, and reason again, until a stop condition — usually the model returning plain text instead of a tool call. A good tool definition needs three things: a precise name, a description that tells the model when to use it — not just what it does — and a typed parameter schema. The main failure modes are infinite loops from missing stop conditions, hallucinated tool names, and tool output poisoning when untrusted content is injected directly into context. The key judgment call: if a single prompt with the right context can answer the question, don't build a loop — loops add latency, cost, and failure surfaces."