PodMan's continual-learning track owns team memory: what the system learns about files, collisions, interventions, outcomes, and future routing for a pod.

Files

File	Purpose
`spec.md`	Data model and observe/store/predict/outcome/adapt loop
`policy.md`	What PodMan may and may not remember
`prompt.md`	Memory-agent prompt for outcome-backed learning
`plan.md`	Demo build order and acceptance criteria

What Is Implemented Now

MongoDB-backed observations, collisions, interventions, outcomes, engineer_states, and team_model records.
Exact signature recall for prior accepted and dismissed outcomes.
Outcome writes through POST /api/outcome.
Team memory graph edges from accepted real outcomes.
No raw screenshots or recordings are stored.

What Is Intentionally Cut

Autonomous model training.
Broad cross-pod generalization.
Raw screen capture retention.
Vector recall as a dependency for the demo proof.

Demo Proof Path

Observe screen/git state -> detect collision -> send intervention -> accept or dismiss outcome -> recall similar event -> show changed graph or changed behavior.