Evaluate any agent framework with Amazon Bedrock AgentCore Evaluations

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AI teams building production agents face a frustrating asymmetry: the diversity of agent frameworks keeps growing, but evaluation tooling has not kept pace. Most evaluation systems assume you built your agent in a specific way: a specific SDK, a specific large language model (LLM) client, a specific tracing pattern. The moment you step outside that narrow compatibility zone, the evaluation pipeline breaks. Teams build on LangGraph for its workflow orchestration model, on LlamaIndex for its tight integration with retrieval pipelines, and on the OpenAI Agents SDK when their organization standardizes on GPT models. They use Google ADK for multi-agent coordination, or the Claude Agent SDK for native Anthropic capability. They reach for Strands Agents because its model-driven loop gets a working agent running on Amazon Bedrock AgentCore in minutes rather than days. And increasingly, they deploy all of these on Amazon Bedrock AgentCore runtime, a capability of Amazon Bedrock AgentCore. It handles the hosting, scaling, memory, and observability infrastructure they would otherwise rebuild for each project. Amazon Bedrock AgentCore evaluations solves this fragmentation by decoupling evaluation from the framework choice. Every major framework supports OpenTelemetry, either natively or through a community instrumentation library. As long as an agent’s telemetry flows through OpenTelemetry, the evaluation service can score it, regardless of what SDK sits underneath. This post explains how that works: what telemetry the service reads, how it decides how to read your spans, which …

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