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Amazon Bedrock Introduces Framework-Agnostic Agent Evaluation with AgentCore Evaluations
Amazon Bedrock's AgentCore Evaluations enables developers to evaluate AI agents across various frameworks using OpenTelemetry telemetry, decoupling evaluation from specific SDKs or models. This approach supports multiple agent frameworks and simplifies the assessment process.
AS1 NewsSource: aws.amazon.com
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REALAI teams developing production agents face challenges due to the proliferation of agent frameworks and the lack of compatible evaluation tools. Amazon Bedrock's new AgentCore Evaluations addresses this issue by supporting a framework-agnostic evaluation process that relies on OpenTelemetry telemetry data.
The evaluation system works by reading specific span roles from traces, such as invoke agent, inference, and execute tool spans, which are produced by various frameworks following the OpenTelemetry or OpenInference conventions. These spans contain the necessary data to reconstruct sessions and score agent performance, regardless of the underlying SDK or framework.
Supported frameworks include LangGraph, LlamaIndex, OpenAI Agents SDK, Google ADK, Claude Agent SDK, and Strands Agents. The system automatically detects and classifies spans based on scope names and attributes, enabling seamless integration without code modifications.
Evaluation can be performed on-demand or in real-time (online), supporting continuous integration pipelines or live traffic monitoring. Scores are consistent across frameworks and modes, facilitating reliable assessment of agent quality.
This framework-agnostic approach simplifies the evaluation process, allowing developers to focus on building agents while ensuring their performance can be accurately measured across diverse implementations.
The new evaluation capability enhances the flexibility and consistency of AI agent assessment across multiple frameworks, supporting better development and deployment practices.