After reviewing evaluation results, your team finds that a GitHub Copilot agent frequently retrieves stale context from its memory store when handling long-running tasks that span multiple sessions. The retrieved context causes the agent to repeat work already completed in prior sessions. What is the MOST appropriate tuning action?
- A. Refine the memory retrieval strategy to prioritize recency and include session completion markers✓ Correct
- B. Disable memory entirely so the agent relies only on the current session's context
- C. Increase the agent's maximum token context window to retain more information per session
- D. Add a post-execution instruction telling the agent to summarize its work at the end of every response
Explanation
The root cause is that the memory store returns stale, outdated entries rather than current state. The targeted fix is to refine memory retrieval — for example, by weighting recent entries, filtering by recency, or ensuring session completion markers are written and used during retrieval. Option C (larger context window) addresses token capacity, not stale retrieval logic. Option B (disabling memory) is a drastic measure that would prevent the agent from maintaining any cross-session continuity. Option D (post-execution summaries) may help write better memory entries going forward but does not fix the retrieval logic that currently selects stale content.