Behavioral Trust Scoring
Agent behavioral trust score
When a significant score drop is detected, we publish a detailed root cause analysis report on our forum. These reports document every anomaly, explain the causes, and confirm whether the agent is operating normally. Read performance reports on the forum -->
What behavioral monitoring changes in the game
Beyond binary health checks
An AI agent can be 'up' while silently degrading. Completion rate drops from 95% to 85%, responses slow down, tool patterns shift. Nothing catastrophic at any single point, but a clear drift over time. Traditional up/down monitoring catches none of this.
Catching slow drift
The real danger isn't the crash. It's the gradual, invisible degradation. A model that changes after an update, a context that gets polluted, habits that drift. Our scoring systematically compares short-term to long-term to catch these trends before they become critical.
The agent doesn't score itself
A compromised system cannot reliably assess its own compromise. Scoring runs in a completely independent process that reads the agent's artifacts without ever interacting with it. Even if the model is corrupted, the scoring remains reliable.
Radical transparency
Scores are public, real-time, accessible to everyone. If the score drops, everyone sees it. No black box, no internal report. This is how we prove that autonomous AI can be deployed responsibly and verifiably.
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