We version control code, review it, test it, and observe it in production. We spent two decades building rigorous lifecycles around it.

Now look at how we treat the context that drives AI coding agents: rules files copy-pasted from blog posts, prompts edited by hand, memory nobody audits. We’re in the cowboy coding era of context.

If context is the primary lever determining what agents produce, it deserves the same engineering rigor we give code. The Context Development Lifecycle (Generate, Evaluate, Distribute, Observe) gives us the stages. The process practices wrap around it: version control, peer review, CI/CD pipelines, and the team workflows to make context a shared engineering responsibility.

Then there’s the bigger picture: the context flywheel. As agents consume context and produce results, every observation feeds back into better context, which produces better results. The teams that get this loop spinning build a compounding advantage that becomes their moat.

This is not a solved problem. It’s a journey we’ve already started, and if the DevOps transition taught us anything, the teams that figure out the lifecycle first will pull ahead fast.​​​​​​​​​​​​​​​​