Tracing AI issues still unclear

Tracing AI problems is hard when the system has many moving parts and very little visibility into what happened between input and output. A user sees a bad answer, but the team needs to figure out whether the issue came from retrieval, prompt logic, model behavior, or tool failure.

Without tracing, debugging becomes a chain of assumptions. With tracing, the team can inspect the exact path the request followed and identify the step where quality began to break down.

Good tracing should capture enough detail to reconstruct the request without making the system impossible to maintain. Once that exists, the debugging process becomes much more concrete.