TripSitter
A supervisory runtime pattern for monitoring autonomous AI sessions, detecting uncertainty or drift, and escalating work back to a human operator when needed.
Autonomy requires active supervision. Systems need ways to notice when they are uncertain, off-policy, over-budget, or operating outside safe bounds.
Problem Space
Agents can continue acting despite uncertainty, stale context, unclear authority, or unstable intermediate state unless a supervisory layer interrupts them.
System Direction
TripSitter studies monitoring, escalation, uncertainty surfacing, and bounded intervention for long-running AI sessions.
Public Capabilities
- 01Drift and uncertainty monitoring
- 02Human escalation patterns
- 03Session supervision
- 04Bounded autonomy controls
- 05Public-safe safety notes
TripSitter is described as a safety and oversight pattern. Internal triggers, thresholds, and runtime enforcement details are not disclosed.
What Is Not Disclosed
Private implementation details, security-sensitive internals, and unreleased runtime architecture are intentionally not disclosed.