Find the First Missing Fact in a Failed Agent Run
Shashank Agarwal
An investigation can stall because the team keeps explaining behavior that the saved evidence cannot distinguish. The next useful action may be a better observation rather than a prompt change.
Quick answer: List plausible explanations for the observed failure and identify which missing fact separates them. Capture that fact in a controlled run or through appropriately scoped instrumentation. Avoid collecting an entire production conversation when a small operation field would answer the question.
Write competing explanations
Imagine an illustrative stock assistant that says an item is unavailable. The request named a branch, but the trace does not show the lookup parameters.
Possible explanations include a wrong branch, an empty provider response or a correct lookup followed by a mistaken explanation. A new model would not tell you which explanation fits.
Build a small evidence decision
| Observation needed | What it helps distinguish |
|---|---|
| Submitted branch identifier | Correct selection versus wrong arguments |
| Provider result classification | Empty result versus failed request |
| Evidence supplied to generation | Lost data versus misinterpreted data |
| Final user-visible response | Internal result versus delivered explanation |
Choose the earliest missing fact that can meaningfully narrow the investigation. This is a proposed debugging method, not a reconstruction of a customer incident.
Capture only what the decision needs
A branch code and a status classification may be sufficient. Full customer addresses or credentials are unlikely to be needed for this question.
If the missing observation cannot be recovered, preserve that limit. A fresh run can test a hypothesis, but it cannot retroactively establish what happened in the original attempt.
Revisit the hypothesis after observation
If recorded arguments identify the correct branch, remove the wrong-branch explanation and inspect the response mapping. Do not continue defending the first plausible story.
Add a regression case once the behavior is understood. The check should exercise the discovered failure rather than merely confirm that a new log field exists.
I'm Shashank Agarwal, founder and CEO of Noveum AI and creator of API.market. These examples describe proposed engineering methods, not customer incidents or measured performance.
Use the tool-boundary guide to define operation evidence and the regression-test guide to preserve a controlled reproduction.
Follow my work on LinkedIn. Drafted with AI assistance.
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