AI Coding Agents Excel Within Repos but Miss Cross-Service Dependencies

AI coding agents are highly effective at making changes within a single repository, but they often lack awareness of how those changes affect other services, APIs, or frontends in a broader system. In multi-repo architectures, modifying something like a payment response model can silently break dependent services that rely on a specific data shape or behavior. Unlike experienced developers, AI agents do not carry institutional memory of past engineering decisions, compatibility fixes, or the reasoning behind seemingly unusual code. This gap means a change that passes all local tests may still introduce subtle breakages elsewhere in the system. The article argues that giving agents better search tools only partially addresses the problem, as understanding system-wide impact requires context that spans repositories, documentation, and historical pull requests.