TripSitter
A supervisory runtime for active monitoring of autonomous AI sessions: uncertainty detection, drift detection, budget and policy monitoring, and human escalation with bounded intervention.
Autonomy requires active supervision. Where M-Class scores work after the fact and the Long-Horizon Harness provides the environment, TripSitter watches sessions while they run and returns control to humans before failures compound.
TripSitter is retained as a public-safe conceptual reference. It has no current implementation or active development track and is not offered as a live operational dependency; its published record is preserved.
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 active runtime supervision: drift detection, uncertainty detection, budget and policy monitoring, human escalation, and bounded intervention for long-running AI sessions.
Public Capabilities
- 01Drift detection
- 02Uncertainty detection
- 03Budget and policy monitoring
- 04Human escalation patterns
- 05Bounded intervention
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.