Run Output Evals Before Shipping Agents
cool-93 · reddit · 2026-07-12
The author believes founders don't necessarily need to "understand how the model works internally." What's more important is establishing a repeatable, engineering-oriented quality assurance process to ensure bad outputs aren't exposed directly to users.
Core practices include:
- Testing with prompts where you already know the "roughly correct answer"
- Covering edge cases, not just the happy path
- Comparing new versions against old ones
- Holding off on release if the new version regresses on tests the old version passed
- Preparing a rollback plan before going live
The emphasis isn't on evaluating models to a researcher's standard, but rather determining if the output is good enough to meet product and user needs. Finally, they ask teams that have deployed agents to production: do you rely more on golden datasets, manual reviews, or automated evals?
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