The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations
AWS Machine Learning published a guide titled "The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations". The material highlights how determining whether a deployed agent works correctly requires addressing several distinct operational questions. AWS Machine Learning outlined an approach for assessing deployed artificial intelligence systems using "Amazon Bedrock AgentCore Evaluations".
Key Takeaways
- The playbook covers evaluation methods including Built-in, DeepEval, and AutoEval to address key questions about agent performance.
When evaluating active agents, developers must look beyond basic completion to assess specific behaviors.
- Key criteria include verifying that an agent finishes its assigned task, remains on topic, avoids data leakage, and refrains from skipping essential steps.
AWS Machine Learning introduced an evaluation playbook covering Built-in, DeepEval, and AutoEval options for Amazon Bedrock AgentCore Evaluations.
- Assessments must also verify whether an agent leaked data or skipped required steps during execution.
- Determining if a deployed agent works requires checking whether it finished the task and stayed on topic.
AWS Machine Learning outlined an approach for assessing deployed artificial intelligence systems using "Amazon Bedrock AgentCore Evaluations". The playbook covers evaluation methods including Built-in, DeepEval, and AutoEval to address key questions about agent performance. When evaluating active agents, developers must look beyond basic completion to assess specific behaviors.
Key criteria include verifying that an agent finishes its assigned task, remains on topic, avoids data leakage, and refrains from skipping essential steps. AWS Machine Learning introduced an evaluation playbook covering Built-in, DeepEval, and AutoEval options for Amazon Bedrock AgentCore Evaluations. Determining if a deployed agent works requires checking whether it finished the task and stayed on topic.
For more details please read the original article at AWS Machine Learning.
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