Why LLM agents break when you give them tools (and what to do about it)

Your agent demo works perfectly. The model picks the right function, passes clean arguments, gets a response, and synthesizes a nice answer. Then you deploy it with 50 real API endpoints and everything falls apart. This is the gap that nobody warns you about in tool-use tutorials. The research on LLM tool use is actually quite mature at this point, with clear findings about what works and what doesn't. But most of those findings haven't made it into the "how to build an AI agent" blog posts that dominate search results. I spent the last few weeks going through the academic literature on tool use in LLM agents. Here's what I found, what it means if you're building agents today, and the failure modes that will bite you in production. The two schools of tool use There are fundamentally two approaches to giving LLMs access to tools, and understanding the difference matters. The first is prompting-based tool use . You describe your tools in the system prompt or via a function-calling API, a