Your First AI Agent Will Fail. Here's How to Debug It.

Your AI agent worked perfectly in testing. Then it hit production and called the wrong tool 14 times in a loop, burned $40 of API credits, and returned gibberish to your user. This is not a rare scenario. It's the default scenario. The reason most developers don't catch this early is simple: they have no visibility into what the agent is actually doing. LLM calls look like black boxes. Tool invocations are invisible. When something goes wrong, you're left reading the final output and guessing backward. This guide gives you four concrete debugging patterns—from zero-setup verbose mode to production-grade tracing with LangSmith. Each one works. Start with the first. Graduate to the fourth when you need it. Why AI Agents Fail Differently Than Regular Code Before the debugging patterns, understand what makes agents hard to debug. In regular code, failures are deterministic: the same input produces the same bug. In AI agents, failures are probabilistic: the same input might work 9 times and