A common failure is context drift, where over many turns the system slowly loses focus as memory or history pushes the model away from the current task. You might see the model referencing outdated topics or mixing unrelated threads. To mitigate, periodically re-anchor by re-inserting summary or system instructions.
Another failure is context overload: too much context (irrelevant docs, long history) crowds out the critical parts. The model may ignore the prompt or hallucinate because the input window is noisy. Solving this requires pruning, summarization, or selective filtering.
A third issue is context rot: memory or context becoming stale or invalid (e.g. user changes preference, domain facts update). If you keep old facts without eviction, the model may use wrong data. To handle it, you version memory, timestamp entries, refresh or evict stale context, and validate against fresh sources. Guarding against these failure modes is a core part of context engineering.