MongoDB plan
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# Agent Learning Policy
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Status: draft
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Scope: guardrails for recursive self-improvement
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## Prime Rule
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PodMan may improve its agent behavior only when the improvement is narrow,
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evidence-backed, versioned, and reversible.
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## Allowed Learning
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PodMan may learn:
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- Which prompt version produces clearer interventions.
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- Which detector threshold reduces false positives.
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- Which routing channel gets accepted without being intrusive.
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- Which verifier best predicts user acceptance.
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- Which graph-discovery rule produces cleaner risk paths.
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## Disallowed Learning
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PodMan must not:
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- Promote a strategy because the model says it is better.
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- Rewrite broad system behavior from one example.
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- Hide failures, dismissals, or rejected candidates.
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- Learn from raw screenshots, secrets, or private terminal content.
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- Turn voice into the default route.
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- Create irreversible actions without human approval.
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## Promotion Rules
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A candidate strategy can become active only when all are true:
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1. It has a parent strategy version.
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2. It describes one concrete behavior change.
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3. It has a verifier plan.
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4. It has evidence from a run, outcome, or test.
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5. It improves or fixes the target metric.
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6. It does not increase user interruption without payoff.
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## Rejection Rules
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Reject and retain the candidate when:
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- The verifier regresses.
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- The change is too broad.
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- The evidence is missing.
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- The candidate conflicts with privacy rules.
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- The candidate makes the demo less stable.
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## Evidence Strength
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| Evidence | Strength | Use |
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| --- | --- | --- |
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| Model opinion | Weak | Proposal only |
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| Trace observation | Medium | Candidate rationale |
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| Human accepted outcome | Strong | Promotion candidate |
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| Human dismissed outcome | Strong | Suppression or rejection |
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| Automated verifier | Strong | Promotion or rejection |
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| Repeated accepted exact signature | Strong | Policy confidence increase |
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## Versioning Rules
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- Strategy versions are immutable after promotion or rejection.
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- There is one active version per `podId + kind`.
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- A rollback activates the previous version; it does not edit history.
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- Parent-child lineage must be preserved.
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## Safety Rules
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- Store summaries, not raw sensitive content.
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- Prefer deterministic checks over model judgment.
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- Use exact MongoDB recall before vector recall.
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- Ask for approval before changing code or data with external effects.
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- Treat hackathon demo stability as a hard constraint.
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## Demo Honesty
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Seeded strategy versions are acceptable when labeled as demo-backed. Do not claim
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a strategy was learned live unless a run and outcome actually created the
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promotion evidence.
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