AI Trust Glossary · Canonical Definition
Continuous Monitoring
Ongoing evaluation of AI agent behavior after deployment - as opposed to one-time testing - enabling detection of drift, failure modes, and degradation before they cause harm.
Explanation
A trust score at deployment is a snapshot. Continuous monitoring turns trust into a live signal. Agents change over time as models are updated, input distributions shift, or operating environments change. Monitoring catches these changes before they become visible failures.
Why it matters
One-time certification is necessary but insufficient. An agent certified at version 1.0 may behave very differently at version 1.5 in production. Continuous monitoring enforces accountability across the full lifecycle, not just at launch.
How Borealis uses it
BTS License Key holders submit periodic telemetry batches via the Merlin SDK. Each batch computes a new BM Score. Score history is tracked in the license_score_history table. Trend analysis across batches enables drift detection before anomaly rates spike.
See also