
Amazon Bedrock AgentCore Evaluations now evaluates agents across all major frameworks.
It reads OpenTelemetry telemetry, ignoring the underlying SDK.
This removes the need to rebuild evaluation pipelines for each framework.
What happened
Amazon Bedrock AgentCore Evaluations now decouples agent evaluation from the framework choice. It reads telemetry via OpenTelemetry, supporting LangGraph, LlamaIndex, OpenAI Agents SDK, Google ADK, Claude Agent SDK, and Strands Agents, with coverage extending to any library using a compliant prefix.
Why it matters
Teams can now score any agent identically (using GoalSuccessRate, Correctness, Helpfulness, or custom LLM-as-a-judge) regardless of the underlying SDK. This removes the need to rebuild evaluation pipelines when switching frameworks, addressing a major source of fragmentation in production agent development.
What to watch
The service relies on spans carrying a session.id matching the runtimeSessionId, and for existing pre-unified observability agents, message content must be included; otherwise response-quality evaluators return an error. Future frameworks just need to install a compliant instrumentation package to be supported.
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The announcement addresses a pain point for teams building production agents: evaluation tooling historically required a specific SDK, LLM client, or tracing pattern. By standardizing on OpenTelemetry, Amazon Bedrock AgentCore Evaluations lets teams switch frameworks without losing the ability to score agent performance. The service reads three span roles—invoke agent, inference, and execute tool—from OpenTelemetry GenAI conventions or OpenInference, bridging both schemas. This means teams can use whatever framework suits their needs—LangGraph for workflow, LlamaIndex for retrieval, or OpenAI Agents SDK for GPT standardization—and still compare results with the same evaluators.
The coverage extends beyond the named frameworks via generic paths for OpenTelemetry or OpenInference instrumentation. Any library that deliberately conforms to these schemas is supported, making it easy for new frameworks to integrate. The main caveat is data grouping: the session.id must match, and message content must be present. For existing agents with pre-unified observability, this requires including the correlated event records; otherwise, response-quality evaluators fail. This is a practical limitation teams should plan for when adopting the service.
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