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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Your IDP is the face of your organisation, not a scaffolding tool
Internal Developer Platforms — Backstage being the obvious one — usually get sold on bootstrapping: click a template, scaffold a new service, get a repo with CI already wired up. That’s a nice party trick. It’s also the least interesting thing an IDP does, because you scaffold a service maybe a handful of times a year.
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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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A local Argo CD playground with kind
The fastest way to build confidence in a GitOps change is to run it somewhere disposable first. I keep a local Argo CD stack on kind (Kubernetes in Docker) for exactly that — a cluster I can create, break, and throw away in minutes.
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GitOps your cloud infrastructure with Crossplane and Argo CD
Most teams already deploy their applications with GitOps: the desired state lives in Git, and a controller like Argo CD reconciles the cluster to match. So why is the infrastructure underneath those apps still provisioned by someone running Terraform from a laptop — or worse, clicking around a cloud console at 2am?
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