An agent runtime is a motivation engine because the runtime determines what context the model sees, which tools it can use, what state persists, and how feedback changes the next action.
Context compaction can preserve goals, decisions, milestones, and open questions across months without forcing an AI to carry every old conversation into each new turn.
AI coaching can fail even with accurate memories when retrieval selects the wrong episode, strips context, or gives stale evidence too much authority.
Retrieval practice becomes a powerful AI game loop when learners repeatedly produce knowledge from memory, receive targeted feedback, and revisit material after spacing.