Memory for Agent Loops
Short answer
Long-running loops need memory strategy: what to keep, what to summarize, what to retrieve, and what to forget.
Why it matters
Without a memory plan, a loop either forgets what it learned or drowns in context. Deciding up front what to persist and what to drop keeps loops both coherent and affordable.
Practical checklist
- Keep durable facts (goal, constraints, decisions)
- Summarize long histories instead of re-sending them
- Retrieve only the observations relevant to the next step
- Forget stale or one-off detail
Example
A multi-day refactor loop keeps a short “decisions so far” note, retrieves the files touched this session, and summarizes everything else — rather than re-reading the whole repo each run.
Common failure modes
No persistence, so the loop relearns each run
Unbounded context growth
Retrieving irrelevant memory that distracts the agent