Coding Agents Need a Live Runtime to Verify Their Changes

AI coding agents can only self-correct when they receive a signal about whether a change actually worked, and passing compilation or unit tests often falls short of that. In cloud-native systems, that signal must come from the running environment rather than local checks. Addy Osmani argues that an agent's surrounding scaffolding, such as prompts, tools and sandboxes, can matter more than the underlying model, citing a case where changing only the harness lifted a coding agent from the top 30 to the top 5 on Terminal Bench. Anthropic's long-running harness work showed feedback loops succeeding when the whole application ran locally, but most production software spans real databases, queues and services that a local machine cannot reproduce. Teams currently work around this gap with mocks, stubs and other partial measures, each of which covers only part of the verification surface.