Tracing and Debugging AI Systems in LlamaIndex

When we build AI systems with LlamaIndex or any other framework, it’s easy to focus on inputs and outputs—a question goes in, and an answer emerges. But much happens beneath the surface: documents are embedded and retrieved, prompts are dynamically constructed, and language models generate results.

In more complex setups, agents may call tools, and workflows may branch based on logic or user interaction. When something goes wrong—a poor answer, a missed tool call, or a slow response—we must understand what happened.

Tracing and debugging help us make the invisible visible. They let us follow how data flows through the system and diagnose exactly where things break down. With LlamaIndex, we can inspect each step—from retrieval and prompt construction to final response generation.