AgentsSeptember 10, 2026via AWS Machine Learning Blog
Agent Evaluation Metric for multi-turn conversations
Why it matters
Agent reliability engineering is moving from 'did the agent succeed?' to 'where did it fail and why?' This is the measurement infrastructure practitioners need to debug and improve production agents.
Key signals
- Agent Evaluation Metric (AEM) introduced for multi-turn conversations
- Turn-level decomposition separates inherited failures from root-cause errors
- First dimension: correctness measurement
- Addresses gap in existing single-turn evaluation approaches
- Published by AWS ML blog (production-grade tooling)
- Agent Evaluation Metric (AEM) introduces turn-level decomposition for multi-turn agent evaluation
- Addresses hidden failure mode: single-turn evals miss cascading errors across conversation turns
- First dimension: correctness measurement to isolate root-cause turns vs inherited failures
- Published by AWS ML blog — suggests enterprise focus on agent reliability at scale
- Practical tool for evaluating agent quality beyond benchmark scores
The hook
One early mistake corrupts every later turn. AWS open-sources a turn-level evaluation metric that pinpoints where multi-turn agents actually break.
Multi-turn agents fail in ways single-turn evaluation misses: one early mistake corrupts every later turn. This post introduces the Agent Evaluation Metric (AEM), a decomposable, turn-level way to measure agent quality, applied to its first dimension, correctness, to pinpoint the turn that caused a …