Use case · CI for AI applications
A release needs a gate that fails honestly.
CI for AI should block only when a check you approved truly falls short — never on a number nobody reviewed. Ask your coding agent to wire CI on your approved checks; the build reads one typed verdict, and a fresh, unapproved run stays informational.
Set up a CI gate for the checks we have reviewed and approved.
What it checks
the verdict from your approved gates · exit code 0 (pass / informational) or 1 (fail / blocked). A gate fires only after a deliberate host approval — there is no gate / approve / certify verb. A fresh run gates nothing and never fails the build.
The scorecard
Verdict pass — a specific approved gate met its threshold. The policy check is informational and does not gate. Illustrative example, not a measured result.
experimental The shipped gate
reads the point threshold; the epistemic core hardens it to decide on the lower confidence
bound — so a tiny-sample perfect score is not enough to pass the default gate
(a named point-smoke policy is the explicit escape hatch). And authority is
capability-typed: a fake or uncalibrated judge produces evidence, not authority, so it
can never move a gate. Confidence & intervals →
What it will not claim
Exit 0 is not “quality verified” — it covers both pass and informational. Green means a specific gate you approved met its threshold, never “safe AI.” The agent can wire the pipeline; only you can approve what gates it. No false green →
Ask your coding agent.
Evaluate my agentic app using EvalGlass.
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