Production Monitoring
Online evaluation, A/B testing, and production metrics.
A/B Testing for Agents
A/B testing for AI agents compares agent versions on live traffic through controlled experiments, but requires larger sample sizes and longer durations than traditional A/B tests due to agent non-determinism and high output variance.
Drift Detection and Model Updates
Agent performance can degrade without any change to your code due to model provider updates, user behavior shifts, and environmental changes – and detecting these silent regressions requires systematic statistical monitoring of quality distributions over time.
Incident Analysis and Evaluation Improvement
Every meaningful production failure should be systematically analyzed, converted into a regression test case, and used to identify gaps in the evaluation suite – creating a feedback loop where incidents continuously strengthen the evaluation system that prevents future incidents.
Online vs Offline Evaluation
Offline evaluation tests agents against fixed datasets before deployment for reproducibility, while online evaluation assesses agents on live traffic under production conditions – and a complete evaluation strategy requires both.
Production Quality Monitoring
Production quality monitoring continuously evaluates live agent interactions through sampling strategies, automated scoring, and anomaly detection to catch quality degradation within hours rather than days.
User Feedback as Evaluation Signal
User feedback – both explicit ratings and implicit behavioral signals like task abandonment and retry patterns – provides irreplaceable evaluation data, but requires careful bias correction because feedback providers are not representative of all users.