How to debug AI agents in production

Traditional software fails the same way twice: same input, same bug, same stack trace. AI agents don't. They are non-deterministic, they call external tools, and they fail in ways that often leave no exception behind. Here is why that makes them hard to debug — and a workflow that works anyway.

Why agents are harder than normal code

Three things break the usual debugging loop. First, non-determinism: the same prompt can take a different path on every run, so "reproduce it locally" often fails. Second, external dependencies: an agent is mostly glue around APIs, and those APIs fail with their own auth, rate-limit, and timeout errors that surface as opaque tool exceptions. Third, silent failures: an agent can return a confident, completely wrong answer without throwing anything at all.

The result is that the single most valuable artifact — the full state of the run that failed — is gone by the time you go looking for it.

Capture this on every run

You cannot debug what you did not record. For each step of an agent run, capture:

• The tool called and its inputs/outputs — what the agent actually did, not what you assume it did.
• The model's reasoning — why it chose that step.
• The raw error— the underlying API response, not the agent's paraphrase.
• Latency and token cost — so you can see slow steps and rate-limit pressure.
• A run status and ordering — so you can replay the run start to finish.

A repeatable workflow

1. Start from the failing run, not your memory. Open the exact run that failed and read its steps in order.
2. Separate symptom from cause. The step that threw is the symptom. Trace the bad input backward to the step that produced it.
3. Classify the failure. Is it auth/permissions, a malformed tool input, a rate limit/timeout, a context problem, or a silent wrong answer? Each class has a different fix. (We break these down in the LangChain root-cause guide.)
4. Diff against a good run. Compare the failing run to the last successful run of the same agent. Divergence localizes the bug faster than reading either run alone.
5. Fix, then verify it stopped. Apply the fix and confirm the same failure fingerprint stops recurring — that is your proof, not the absence of a crash on one retry.

Make the loop automatic

Doing all of this by hand — wiring up tracing, preserving raw errors, replaying runs, diffing against a baseline — is most of the work. That is what Vorlo automates: two lines of code capture every run, failures are translated into a plain-English root cause and a specific fix, and you can ask "why did my last run fail?" from your editor and get the answer with the failing step in context. Failures even find you first, via a Slack alert the moment a new failure pattern appears.