Structural observability for AI conversations. Detects loops, stuck states, breakthroughs, and convergence across 17 channels without analyzing content.
Provides AI agents with real-time cognitive health monitoring, detecting context rot through token utilization, retrieval accuracy, and session fatigue analysis.
A visual canary that detects context rot and silent model degradation in long agent conversations by embedding externally verified checkpoints and self-reported status into each response.
Behavioral governance layer for AI assistants that monitors for hallucination, inconsistency, and unsafe reasoning patterns while managing stateful AI sessions.