📚 AI Agent Evaluation Series - Part 4 of 5

1. Observability & Evals: Why They Matter ←
2. Human-in-the-Loop Evaluation ←
3. Implementing Automated Evals ←
4. Debugging AI Agents ← You are here
5. Human Review Training Guide →

Debugging AI Agents with Langfuse: Observability & Evals That Actually Work

Building AI agents is exciting. Debugging them when they fail in production? Not so much.

Here's the problem: AI agents don't fail like traditional software. There's no stack trace pointing to line 47. Instead, you get vague responses, hallucinations, or worse—confidently incorrect answers. Your users see the failure, but you have no idea why the agent decided to call the wrong tool, ignore context, or make up facts.

The solution? Observability and evaluation systems built specifically for AI.

In this guide, we'll show you how to use Langfuse to debug AI agents effectively. You'll learn how to trace agent execution, analyze LLM calls, build evaluation datasets, and implement automated checks that catch issues before your users do. Whether you're running simple RAG pipelines or complex multi-agent systems, these techniques will help you ship reliable AI applications.