Why AI-screened CI/CD beats every gate you've already tried
Linters, code review, e2e tests, canaries — and yet bad deploys still ship. Here's why screening at the model layer changes the game.
Every CI pipeline is a stack of gates: lint, type, unit, integration, e2e, canary, rollback. Each one catches a slice of bad change. None of them catch the one that matters most — the change that compiles, tests green, and still takes production down at 4:00 PM on a Friday.
The class of bug your tests can't see
The deploys that hurt aren't the obviously broken ones. They're the ones that look fine in isolation but interact badly with live traffic, warm caches, or a downstream dependency that's been quietly degrading for a week. By construction, your test suite has never seen that exact world.
What a model can see that a test can't
An AI screener doesn't run your test suite — it reads the diff against the last 90 days of deploy telemetry, the code graph, and the production traces. It flags 'this PR looks like that PR from March that took the checkout flow down for 18 minutes' before a human is on the hook.
Blast radius, not pass/fail
The output isn't a binary. It's a blast-radius score, a confidence band, and a human-readable rationale. Reviewers stop arguing about whether to merge and start arguing about how to merge — behind a flag, to one region first, with a tighter rollback budget.
Why this is finally possible
Two things changed: deploy telemetry is finally structured enough to be a training signal, and models are finally fast enough to give a useful answer inside a PR check's timeout. We built OpsDevAI around exactly that bet.
Self-healing clusters: the boring version of an exciting idea
Auto-remediation isn't an AI fantasy — it's a tight feedback loop between detection, attribution, and policy. Here's how we built ours.