Teams building AI agents on AWS use a range of frameworks, including LangGraph, LangChain, Strands Agents, and Amazon Bedrock AgentCore. Each framework records agent execution in its own way, so the structure of messages, tool calls, and handoffs between agents differs from one to the next. Evaluation tooling tends to follow the same boundaries: teams build metrics, scoring logic, and result storage against a single framework’s trace format, and they must rebuild them when they switch frameworks or run agents on more than one.

This coupling makes a basic development question harder to answer: did a change to the prompt, model, or tools improve the agent? Answering it requires consistent metrics across versions of an agent and a shared record of past results to compare against. Agent quality also spans several dimensions that a single accuracy score does not capture: tool calling, response quality, responsible AI, performance, multi-turn conversation, multi-agent coordination, and reasoning.

In this post, we introduce the Universal Agent Evaluation Framework (UAEF). Customers across 12 organizations in the telecom, healthcare, and financial services industries used UAEF to evaluate their AI agents with one consistent set of metrics and compare agent versions before release. UAEF is a framework-agnostic evaluation system: it uses adapters to convert traces from each framework into a common format, then scores those traces with 36 built-in metrics across 7 dimensions. It stores the results in Amazon DynamoDB and Amazon Simple Storage Service (Amazon S3) and generates reports and insights that inform the next iteration of the agent.

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