Coding agents self-correct only when something tells them whether a change worked. In a cloud-native system, that signal comes from a running environment, not from local checks.

Every coding agent reaches the same moment. The code compiles, the unit tests pass, and the agent declares the task done. Whether it is done depends on something the agent rarely sees: how the change behaves inside the running system. That question is the hard part of verifying AI-generated code on Kubernetes .

Addy Osmani’s writing on agent harness engineering makes an argument worth taking seriously. An agent is a model plus the scaffolding around it: prompts, tools, context policies, execution sandboxes, subagents, hooks and feedback loops. That scaffolding often moves results more than a model upgrade does. Addy points to Viv Trivedy’s team, which moved a coding agent from the top 30 to the top 5 on Terminal Bench by changing only the harness.

The argument holds.…