AI in the CI/CD loop: let the gates do the trusting
AI coding agents are good enough now that writing the code is no longer the bottleneck — trusting what gets shipped is. “It compiles” and “it looks right to the model” are not quality gates. If you let an agent merge and deploy on its own judgement, you’ve quietly replaced code review and testing with vibes. The fix isn’t to keep AI out of production. It’s to make it pass exactly the same gates a human would.
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From commit to production: how Kargo completes the GitOps flow
Most teams that adopt GitOps end up with two-thirds of a delivery pipeline. CI builds and pushes an image. Argo CD reconciles the cluster to whatever is declared in Git. Both halves are solid. But the bit in the middle — moving a change through dev, staging and production, with checks at each step — is almost always a pile of bespoke scripts, manual pull requests, and “just bump the tag in the prod values file.” That’s where Kargo comes in.
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