Question 1 · choose 1
Three hours into exploring a large legacy codebase, your agent starts giving answers that contradict its earlier findings and refers to "typical repository patterns" instead of the specific classes it identified in the first hour. Later questions build on those findings. What practice best counters this?
- AHave the agent keep a scratchpad file of key findings and consult it for later questions
- BTurn off automatic compaction so that none of the earlier exploration output is ever summarized away
- CRaise the effort level so the agent reasons more carefully
- DPaste a reminder of the earlier findings into each new prompt by hand as the session goes on
Show the answer and why
AHave the agent keep a scratchpad file of key findings and consult it for later questions
Correct
Notes persisted outside the context window survive degradation and compaction and can be reread for each new question.
BTurn off automatic compaction so that none of the earlier exploration output is ever summarized away
Incorrect
Without compaction the context keeps filling with verbose output until the session fails.
CRaise the effort level so the agent reasons more carefully
Incorrect
More reasoning per response does not restore details that are buried or lost in a degraded context.
DPaste a reminder of the earlier findings into each new prompt by hand as the session goes on
Incorrect
Manual reminders depend on someone remembering what mattered and add more text to an already long context.
In long explorations, models drift toward generic answers as context degrades. A scratchpad of key findings that the agent maintains and rereads preserves specifics across context boundaries.
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