Anuncio

SRExpert

Deja de saltar entre 6 herramientas de Kubernetes.

Monitoreo, alertas, seguridad, despliegues Helm y operaciones con IA en una sola plataforma.

Probar 30 días gratis

The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations

1d

Antigüedad

Publicado
Recolectado

When you deploy an agent, whether it is working is really several questions: did it finish the task, stay on topic, leak data, or skip steps? This post shows how to evaluate agents on Amazon Bedrock AgentCore with built-in, DeepEval, and AutoEval evaluators, and how to match each behavior that matters to the right evaluator at the right level.

Extracto de la fuente

When you deploy an agent to production, one of the hardest questions is whether it’s actually working? The bar isn’t whether it runs. It’s whether it does what the business needs, at scale. Many teams will tell you they can’t really see this. They spot-check outputs, watch latency, and collect user feedback when they can get it. None of that tells you whether the agent finished the user’s task, stayed on topic across a long session, leaked personally identifiable information (PII), or quietly…

overfeed.news indexa y enlaza. Publicamos un extracto breve; el artículo completo sigue en AWS Machine Learning Blog.

Inicia sesión para seguir esta fuente
The agent evaluation playbook: Built-in, DeepEval, and AutoEval…