Engineering insights on AI reliability, AI SRE, system reliability, production resilience, and the future of DevOps.
In March 2026, Amazon lost 6.3 million orders in a single afternoon. The cause wasn't an AI agent. It wasn't AI-generated code either. It was both - running on top of each other. This is the failure surface nobody's measuring yet.
Anthropic's model card includes a candid reliability assessment of Claude Mythos. We break down what their own engineers found - where it's a step change, where it still falls short, and what this means for teams running AI in production.
Mid-investigation, a tool call comes back empty. One agent retries endlessly. Another reports a failure. A third keeps searching across every data source it can find. Same empty response, three completely different problems.
AI SRE did not appear out of nowhere. Here's where it came from, how the market is forming, and why the next phase will be about prevention, not prettier incident response.
We use AI coding tools every day. They're the future. But 46% AI-generated code, 98% more PRs, and 30% more change failures is what that future actually looks like - and nobody's talking about it honestly.
Most companies think they have an AI problem. They don't. They have a data problem - messy pipelines, no context, garbage semantics - and they're throwing models at it hoping nobody notices.
We automated detection twenty years ago (to some extent...) - Investigation is still manual. Here's why that's a problem.
