Agent Evaluation Metric for multi-turn conversations
AWS Machine Learningen

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 failure and separate it from the turns that inherited it.
This is a short summary published by AI Global Wire. The full article is owned and hosted by AWS Machine Learning — open it there to read it in full.
Read the full story at AWS Machine Learning- Verktyg
- Agenter
- Företag
Related AI news
- Arlequin AI raises €28M to build novel AI models that learn complex relationships at scaleSiliconANGLE · September 10, 2026
- Swarmchasers" hunt rogue agents, Anthropic investigates itself, and the trail they both follow is going darkThe Decoder · September 10, 2026
- Former Deepmind PR staffer says the lab once banned public discussion of AI extinction riskThe Decoder · September 10, 2026
- Model-agnostic PII detection with LLMsAWS Machine Learning · September 10, 2026
- Atlassian upgrades AI coding agents for always-on software developmentSiliconANGLE · September 10, 2026
- How AvioBook builds turnaround insights from operational data with Amazon Bedrock AgentCoreAWS Machine Learning · September 10, 2026