Ramis
186 lines
3.7 KiB
Markdown
186 lines
3.7 KiB
Markdown
# Agent Learning Spec
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Status: draft
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Scope: how PodMan agents improve their own prompts, policies, detectors, and routing behavior
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Owner: agent learning / recursive self-improvement
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## Purpose
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Agent learning is the recursive self-improvement layer. It is not the same as
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team memory. Team memory learns about engineers and work. Agent learning learns
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which agent strategies produce better outcomes.
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The demo claim:
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1. PodMan tries a coordination strategy.
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2. The run is traced in MongoDB.
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3. A verifier or human outcome scores it.
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4. Gemini or another agent proposes a narrow strategy change.
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5. The new strategy is versioned.
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6. A later run uses the improved strategy and shows a better result.
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## Core Objects
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### Agent run
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One attempt to execute a goal.
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```text
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agent_runs
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runId
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podId
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goal
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trigger
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strategyVersionId
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status
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startedAt
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completedAt
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score
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verifierSummary
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inputRefs
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outputRefs
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```
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Allowed `status` values:
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```text
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running, succeeded, failed, improved, regressed, abandoned
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```
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### Trace event
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Append-only event log for a run.
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```text
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agent_trace_events
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runId
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podId
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step
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phase
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eventType
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inputSummary
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outputSummary
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toolName
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error
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metrics
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createdAt
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```
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### Strategy version
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Versioned prompt, detector rule, policy, verifier, or routing strategy.
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```text
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strategy_versions
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strategyVersionId
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podId
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kind
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name
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parentVersionId
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status
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summary
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promptText
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policy
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verifier
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metrics
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createdAt
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promotedAt
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```
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Allowed `kind` values:
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```text
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prompt, policy, detector, verifier, routing
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```
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Allowed `status` values:
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```text
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candidate, active, retired, rejected
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```
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### Learning proposal
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A candidate change before promotion.
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```text
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learning_proposals
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proposalId
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podId
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sourceRunId
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targetKind
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parentVersionId
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proposedChange
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rationale
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verifierPlan
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status
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createdAt
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resolvedAt
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```
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Allowed `status` values:
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```text
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open, accepted, rejected, superseded
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```
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## MongoDB Indexes
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| Collection | Index | Purpose |
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| --- | --- | --- |
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| `agent_runs` | `{ podId: 1, startedAt: -1 }` | Recent run history |
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| `agent_runs` | `{ podId: 1, strategyVersionId: 1 }` | Compare strategy performance |
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| `agent_trace_events` | `{ runId: 1, step: 1 }` | Reconstruct run |
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| `strategy_versions` | `{ podId: 1, kind: 1, status: 1 }` | Find active strategy |
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| `strategy_versions` | `{ podId: 1, createdAt: -1 }` | Version history |
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| `learning_proposals` | `{ podId: 1, status: 1 }` | Open candidate changes |
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## Learning Loop
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```text
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observe run -> score run -> propose change -> test candidate -> promote or reject
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```
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Agent learning must always connect these records:
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```text
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agent_run -> trace_events -> verifier result -> learning_proposal -> strategy_version
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```
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## Verifier Contract
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Every promoted strategy needs a verifier signal.
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Allowed verifier types:
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- Human accepted or dismissed outcome.
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- Test pass or fail result.
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- Reduced false positive rate.
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- Reduced intervention count with same or better accepted outcomes.
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- Faster successful run.
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- Better graph discovery precision.
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- Explicit demo operator approval.
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Self-evaluation alone is not enough to promote a strategy.
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## Relationship to Team Graph
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Agent learning can appear in the Team memory graph as activity and loop status,
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but it should not clutter the main risk graph by default.
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Graph discovery may show:
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- `agent_run` activity in the stream.
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- `strategy_versions` count in the learning loop.
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- A selected-node detail saying a policy changed because a prior outcome was
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dismissed or accepted.
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## Acceptance Criteria
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- Every strategy change has a parent.
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- Every promoted strategy cites evidence.
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- Rejected strategies are retained with a reason.
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- Agent traces are append-only.
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- The system can answer: "What changed, why, and did it help?"
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