The agent you can't debug
When a user says the agent did the wrong thing and the original run wasn't traced, you're left re-running and guessing, because there's no record of the prompts, tool calls, and results that produced the behavior.
A user reports a bad answer, the run wasn't recorded, and the team spends an afternoon trying to reproduce something they can't see.
where_are_you
The maturity ladder, worst to best
These are the rungs the Agent Reliability Scorecard scores for this dimension, straight from the tool. Find where your agent sits, then aim for the top rung.
We re-run it and guess; the original run isn't recorded
We have logs of the final output, but not the steps
Each step (prompt, tool call, result) is traced and inspectable
Full step traces, and eval suites that would have caught ittarget
We try a couple of cases by hand
A short manual checklist before release
A saved eval set we run before each release
Automated evals and output guardrails gate every change in CItarget
why_it_matters
An agent's behavior is emergent: it comes out of a chain of prompts, tool calls, and results that you don't control step by step. When something goes wrong, the only way to understand it is to see that chain. If all you kept was the final output, you're reduced to re-running the task and hoping it misbehaves the same way, which for a non-deterministic system it often won't. The run that mattered is gone.
Make every run replayable and every change tested. Trace each step, the prompt, the tool call, the result, so you can see why the agent acted, not just what it returned. Then gate prompt, model, and tool changes behind an eval suite, so a change can't silently regress behavior that used to work. Observability and evals are what turn "the agent feels flaky" into a specific step you can fix and a test that keeps it fixed.
more_failure_modes
Related ways agents break
The confident wrong answer
Read itContext managementThe agent that forgets the goal
Read itSee all 7 failure modes, or score your loop with the scorecard.
faq
Questions & answers
- How do you debug an AI agent that did the wrong thing?
- You need the trace of the run: the prompt, tool call, and result at each step. With only the final output you're stuck re-running a non-deterministic system and guessing. Trace every step so you can see why the agent acted, and keep an eval suite so you can reproduce and lock the fix.
- What should you log for an AI agent?
- Each step of the loop: the prompt sent, the tool called with its arguments, and the result returned, tied to a run id. That's what lets you replay a bad run instead of re-running and hoping. Pair the traces with an eval set that gates prompt, model, and tool changes so regressions are caught before release.
Fixing one failure mode is a day. Hardening the whole loop is the work.
I review where your agent loop terminates, how it escalates, how it fences tool output, idempotency on the side effects, and the evals that catch a regression before users do. Book a call, or leave your email.
Prefer proof first? See how this plays out in real case studies →