Agent Supervision
The practice of monitoring, reviewing, and governing AI agent behavior in production. Supervision includes real-time monitoring of agent actions, quality sampling of outputs, performance metric tracking (accuracy, resolution rate, cost per action), drift detection (quality degradation over time), and incident response when agents behave unexpectedly. Supervision is the operational layer that ensures agents remain reliable and aligned with business goals after deployment.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A team deploys a support agent with a supervision dashboard: every response is logged with confidence score, resolution outcome, and customer satisfaction rating. A daily quality review samples 50 conversations. Automated alerts fire when resolution rate drops below 70% or when the agent attempts an action outside its approved scope.
Frequently asked questions
- How much supervision do AI agents need?
- Inversely proportional to deployment maturity and stakes. New agents: review 100% of actions in the first week, then 20-30% in weeks 2-4. Established agents: 5-10% random sampling plus automated quality checks. High-stakes domains (healthcare, finance, legal): maintain higher review rates indefinitely. The goal is to reduce supervision cost over time as the agent proves reliable—not to eliminate it entirely.