How do you detect agent drift?

Monitor key metrics over time: resolution rate, customer satisfaction scores, escalation rate, and hallucination rate. Set up weekly automated evaluations using a golden dataset of test queries with known-correct answers. A 5%+ decline in any metric over 30 days signals drift that needs investigation.

Agent Drift

Written by Max Zeshut

Founder at Agentmelt

The gradual degradation of an AI agent's performance over time as the underlying data, user behavior, or business context changes while the agent's configuration remains static. Drift manifests as declining accuracy, increased hallucination rates, or irrelevant responses—often so gradually that it is not noticed until performance has significantly deteriorated. Common causes include knowledge base staleness, shifting customer vocabulary, product catalog changes, and upstream model updates by the LLM provider.

Example

A support agent trained on last year's product documentation continues answering questions about a feature that was deprecated three months ago—confidently providing instructions for functionality that no longer exists.

Frequently asked questions

How do you detect agent drift?: Monitor key metrics over time: resolution rate, customer satisfaction scores, escalation rate, and hallucination rate. Set up weekly automated evaluations using a golden dataset of test queries with known-correct answers. A 5%+ decline in any metric over 30 days signals drift that needs investigation.

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