Evaluate Amazon Bedrock Agents with Ragas and LLM-as-a-judge
AWS presents an open-source Bedrock Agent Evaluation approach that combines Ragas metrics, LLM-as-a-judge scoring, and Langfuse traces.
Quick read
2 bullets- AWS presents an open-source Bedrock Agent Evaluation approach that combines Ragas metrics, LLM-as-a-judge scoring, and Langfuse traces.
- The example evaluates Amazon Bedrock Agents across RAG, text-to-SQL, custom tool use, and chain-of-thought capabilities, using a pharmaceutical research workflow.
Why it matters
Production agents need repeatable evidence about task success, faithfulness, relevance, and tool behavior. This workflow connects those measurements to traces and dashboards, making evaluation part of the development loop instead of a one-off demo check.
Builder takeaway
AWS published this update in the AWS lane. Use the original source for details, then compare it with related briefings before changing a roadmap, workflow, or production system.
Production agents need repeatable evidence about task success, faithfulness, relevance, and tool behavior. This workflow connects those measurements to traces and dashboards, making evaluation part of the development loop instead of a one-off demo check.
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