What Is Tool Chaining in LLMs? Why It Breaks and How to Think About Orchestration

Your agent chains three tool calls together. The first returns slightly malformed output. The second accepts it but misinterprets a field. By the third call, the entire chain has gone off the rails. No error was thrown. Your logs look clean. The user got confidently wrong answers. If you've built anything with LLM agents beyond a demo, you've hit this. It's called the cascading failure problem, and research from Zhu et al. (2025) confirms it: error propagation from early mistakes cascading into later failures is the single biggest barrier to building dependable LLM agents. I've spent a lot of time debugging these kinds of failures, and I want to break down why tool chaining is so fragile, what the actual failure modes look like, and what patterns hold up in production. Tool Chaining, Quickly Defined Tool chaining is when an LLM agent executes multiple tool calls in sequence, where each tool's output becomes input for the next. The agent gets a user query, calls an API, processes the re