4. Why AI coding agents make mistakes

A coding agent working on the same feature faces the same gaps. The agent sees the codebase, the ticket and the wiki, and the wiki is often stale. The agent cannot see the direct messages, does not know which custodian to ask, and cannot ask anyway. Everything the four custodians know is invisible to the agent. In one example, an agent called a function the team had removed three sprints earlier, because the spec said the function existed. Coding agents do well on small, contained tasks, and make mistakes on the complexity of enterprise applications. On one codebase of more than two million lines, the team's AI coding tools produced isolated prototypes and no overall gain in productivity. On another team, an agent working from an out-of-date wiki needed so much back-and-forth that the change took longer than it would have taken by hand. Bigger context windows do not solve the problem, because more unstructured text gives the agent nothing it can check its work against. Building new applications, changing existing ones and modernizing legacy systems all face this gap, at different levels of complexity. The agent needs what the four custodians know written down in a structured form, which it can search for exactly the part each task needs.

On the site: The Shared Problem · Why AI Coding Agents Stall on This · The Manual Translation Tax: The Cost, Named