Passing Yesterday's Agent Dataset Is a Starting Point
Shashank Agarwal
A passing dataset establishes a result for its cases and execution conditions. It does not establish that every new user request, tool version or policy change is covered.
Quick answer: Retain a stable regression set and review coverage whenever the application changes. Add cases for new responsibilities and observed gaps, record the dataset version and report results for stable and newly added cases separately. Treat changes in production inputs as a reason to investigate, not automatic proof that quality declined.
Identify what changed outside the prompt
Consider an illustrative catalog assistant. Its original tests cover single-location stock lookup. The product later adds transfers between locations. A perfect result on the old lookup set says little about transfer authorization or partial completion.
Review changes to the task, tool schema, source material, permission boundaries and user population. Each can invalidate an assumption without changing the model itself.
Keep a coverage record
| Change | Question for the dataset |
|---|---|
| New operation | Is its successful and unsuccessful behavior represented? |
| Changed policy | Which expected decisions need an explicit revision? |
| New response fields | Can current checks interpret them? |
| Different request patterns | Which observed patterns deserve controlled cases? |
Do not overwrite an old expectation without preserving its context. A policy revision and a regression are different explanations for a changed result.
Preserve a comparable baseline
Report the candidate and baseline on the same stable set, then show the new coverage separately. If you combine results, explain the composition. An easier expanded dataset can improve an aggregate even while old cases regress.
Retain failure examples and execution conditions. If a case depends on changing external data, use a controlled fixture where possible and describe the remaining variability.
Use monitoring to propose cases
Review appropriately minimized production evidence for gaps. Reconstruct a synthetic case when possible; do not publish or indiscriminately copy private telemetry into an evaluation set.
I'm Shashank Agarwal, founder and CEO of Noveum AI and creator of API.market. I recommend a dataset change log that explains which decision each addition supports.
The starter dataset guide covers initial composition. The regression-test guide describes turning a specific failure into controlled coverage.
Follow my work on LinkedIn. Drafted with AI assistance.
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