Coding agents have become extremely powerful and capable of handling a wide range of tasks such as planning, writing code, refactoring, and debugging. Their syntax is often flawless, and they can reason through complex problems with speed and consistency. In many cases, they can outperform humans on well-scoped, technical tasks.
One of their biggest limitations, however, is memory. Coding agents tend to have very short working memories, and they struggle to retain context over long sessions, across files, or between iterations. This is not a problem that will be fully solved simply by using larger or more powerful models. Agents can forget earlier decisions, assumptions, or constraints and may confidently produce solutions that conflict with prior work.
A practical way to address this limitation is by intentionally creating and managing context. This means breaking large tasks into small, well-defined chunks that an agent can complete independently. Each step should result in clear documentation, where it records what was built, why certain choices were made, and what remains unresolved. Test cases become especially important here, as they act as a persistent source of truth that helps future agents validate behavior and avoid regressions. This information will allow another agent (or the same agent in a later session) to pick up where the previous one left off.
What other challenges have you encountered when working with coding agents?