Yahya Alhinai
89 lines
1.9 KiB
Markdown
89 lines
1.9 KiB
Markdown
# Agent Learning Plan
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Status: planned / narrow v1
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Goal: ship a visible recursive self-improvement loop without overbuilding
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## Must-Have
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1. Store agent runs.
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2. Store trace summaries.
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3. Store active and candidate strategy versions.
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4. Attach verifier or outcome evidence.
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5. Show one strategy improvement in the demo narrative.
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## Build Order
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### R1: Trace the run
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Write one `agent_runs` record for an important coordination decision and append
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trace events for:
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- observation
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- recall
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- prediction
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- intervention
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- outcome
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- adaptation
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### R2: Version the strategy
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Create an active strategy version for one of:
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- collision detector threshold
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- intervention routing
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- graph discovery filter
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- card wording prompt
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### R3: Score the outcome
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Use the simplest verifier:
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- accepted real collision = useful
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- dismissed = noisy
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- no response after cooldown = uncertain
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### R4: Propose a narrow change
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Examples:
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- "For this exact signature, prefer sync PR card."
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- "For dismissed docs-only overlaps, suppress voice escalation."
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- "For repeated auth.ts collisions, raise severity."
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### R5: Promote or reject
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Promote only when evidence is strong enough. Otherwise keep the candidate as
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rejected or open.
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## Demo Path
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1. Show baseline strategy.
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2. Trigger a collision.
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3. Accept or dismiss the intervention.
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4. Store outcome.
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5. Show a candidate strategy update.
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6. Promote it.
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7. Trigger a similar event.
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8. Show changed behavior.
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## Nice-to-Have
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- Strategy comparison panel.
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- Model-generated prompt patch with verifier.
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- Vector recall over strategy history.
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- Rollback UI.
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## Cut
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- Full autonomous code rewriting.
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- Multi-agent strategy debates.
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- Long-term benchmark suite.
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- Training a model.
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## Acceptance Criteria
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- The demo can point to a MongoDB record proving the agent changed behavior.
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- The changed behavior is visible.
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- The strategy has a parent and evidence.
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- Rejected or failed changes are not deleted.
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