AI Agents In Practice: Reading Failures from The Trace
Your AI agent does not crash. It reports success. But your bank account shows a mistake.
A refund went out for an order that was never cancelled. The customer has the item and the money. The agent thought it did its job.
Do not reach for a bigger model. Do not just add a retry loop. Both are guesses.
Instead, read the trace. The agent already wrote down what it did.
A good production trace records the loop step by step. It must show:
- What the agent observed
- What it decided
- Which tool it called
- What the tool returned
- The verification read from the source of truth
- The final state and the cost
The most important part is the gap between the tool response and the verification read. A tool might say "accepted," but that does not mean the world changed. The verification read tells you if the change actually happened.
Failures usually fall into two groups:
- Execution Failures
- Tool failures: Bad arguments or timeouts.
- Reasoning failures: The model chose the wrong action.
- Control-state failures: The agent believes a lie. It thinks an order is cancelled because the tool said so, even if the database says otherwise.
- Structural Loop Failures
- Context degradation: The agent loses the thread.
- Loop runaway: The agent repeats steps without progress.
- Silent stalls: The agent hangs without an error. You need a watchdog to treat silence as a failure.
When you find a failure, do not just retry. Retry is a strategy, not a diagnosis.
- If it is a transient error like a timeout, retry.
- If it is a logic error, retrying just spends your budget to hit the same wall.
- If the agent hits a blocker, stop and tell a human.
The best way to fix a failure is to turn it into a test.
Use the trace to write a grader. If an agent failed to verify a cancellation, write a test that fails if a refund happens without a confirmed cancelled status. Turn the failures you paid for into failures you never pay for twice.
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