AI & Technical question

Define the minimum product foundations and quality bar for a trusted app ecosystem in ChatGPT and Codex. How would you handle model-behavior variability, tool-call reliability, API or SDK limitations, fallback behavior, evals, and partner certification so users get a consistently safe and useful experience?

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What this question tests

Whether you can define the technical and trust foundations required for a reliable, safe app ecosystem built on an inherently variable model.

Set the quality floor: define acceptable variance bounds for model behavior across common tasks, tested through a standard eval suite every partner app is checked against.
Require reliable tool-call behavior: schema validation, retries with clear failure states, and a documented contract for how partners handle tool errors.
Define fallback behavior explicitly: what the experience looks like when a model or tool call fails, so users see a clear, honest failure state rather than a silent or misleading one.
Set API and SDK constraints partners must design within, for example rate limits and supported capabilities, published clearly so partner apps do not silently break on model updates.
Build a partner certification process: a required eval and review pass before an app is listed, plus a lighter recheck after major model updates.
Combine automated evals (regression suite run on every model update) with a review process for higher-risk categories (health, finance, legal) before certification.

Names a concrete mechanism for model-update risk: a required recheck of certified apps after major model behavior changes, since variability is the core risk named in the question.
Specifies fallback UX as a requirement, not an afterthought, since silent failure is the worst outcome for user trust.
Tiers certification rigor by risk category, treating health or finance apps with a higher bar than a simple productivity app.
Ties tool-call reliability to a concrete technical contract (schema validation, defined retry and failure behavior) rather than a vague reliability promise.

Treating certification as a one-time gate with no recheck after model updates change behavior.
No defined fallback UX for tool or model failures.
Applying the same certification bar to every category regardless of risk.

How would you handle a partner app that was certified but starts failing after a model update?
What would the certification process look like for a high-risk category like health advice?
How would you balance developer velocity against the cost of a rigorous certification process?