Golden Datasets for AI Agents
The curated set of real cases with known-good answers that every agent eval suite is built on.
A golden dataset is a curated set of inputs paired with known-good expected outputs, used to test an AI agent the same way on every change. It is the backbone of agent evaluation: without it, you are testing on vibes. The best golden datasets are grown from real production cases — especially the ones that went wrong — so they stay representative of what the agent actually faces.
Why do AI agents need a golden dataset?
Because agents are non-deterministic and you ship changes constantly. A golden dataset lets you replay the same cases through every new version and see whether quality went up or down — turning 'did that prompt change help?' into a measured pass rate instead of a guess. It is also your regression net: the change that fixes one case often quietly breaks another, and only a fixed dataset catches it.
How do you build a golden dataset for an agent?
Start with twenty real cases pulled from production logs, support escalations or your own testing. For each, capture the input and the expected outcome — for a support agent, the customer message and the correct resolution; for a RAG agent, the question and the source passage that answers it. Prefer real cases over synthetic ones; they encode the weirdness of actual usage. Then grow the set from reality: every production failure, bad-feedback session or incident becomes a new case. Within a few months you have one to two hundred meaningful cases, each earned from a real failure.
How big should a golden dataset be?
Smaller than teams fear. Twenty to fifty cases that cover your core task, your top failure modes, and a few adversarial or out-of-scope inputs is a defensible start. What matters is coverage of consequence, not raw count: fifty cases that include every action your agent can take with real-world side effects beat five hundred variations of the happy path. Let production grow it from there.
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Prefactor gives enterprises runtime governance, observability, and control over every AI agent in production.
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