Summary
Agents are stateless. A DeepResearchAgent that discovers a useful decomposition pattern doesn't share it with future runs. Knowledge is lost at session end.
Proposal
Add a memory layer that persists useful patterns across agent runs:
- Pattern storage (successful strategies, useful decompositions)
- Retrieval at agent start (seed new runs with past learnings)
- Decay/relevance scoring (old patterns may become stale)
Open Questions
- Where to store? (SQLite, filesystem, MCP resource?)
- What to store? (Explicit agent outputs? Implicit behavioral patterns?)
- How to retrieve? (Semantic search? Structured queries?)
- How to avoid context bloat? (Summarization? Relevance filtering?)
Context
From user_guide.md "What to Build Next" - Priority 2.
Suggested by gemicro session during cross-session Frontier feedback.
Summary
Agents are stateless. A DeepResearchAgent that discovers a useful decomposition pattern doesn't share it with future runs. Knowledge is lost at session end.
Proposal
Add a memory layer that persists useful patterns across agent runs:
Open Questions
Context
From user_guide.md "What to Build Next" - Priority 2.
Suggested by gemicro session during cross-session Frontier feedback.