AWS releases UAEF, a framework-agnostic evaluation system for AI agents
AWS has released UAEF, a framework-independent tool for evaluating AI agents with 36 metrics across 7 dimensions. It normalizes outputs from different frameworks (LangGraph, LangChain, Strands Agents, Bedrock AgentCore) into a single format and was tested by 12 organizations.
AWS has introduced the Universal Agent Evaluation Framework (UAEF) — a system for evaluating AI agents that is independent of any specific framework. It addresses the problem that teams building agents on LangGraph, LangChain, Strands Agents, or Amazon Bedrock AgentCore previously had to build metrics and evaluation logic separately for each framework's record format, and had to rebuild them when switching to a different framework.
UAEF uses adapters that convert agent run records (messages, tool calls, handoffs between agents) into a unified format called AgentTrace. Using this unified record, the evaluation engine then calculates scores from 36 built-in metrics divided into 7 quality dimensions: tool calling, response quality, responsible AI (safety, bias, toxicity, resistance to prompt injection), performance (latency, token consumption, throughput), multi-turn conversation, multi-agent coordination, and reasoning. Before storing data, the system, according to the company, checks for the presence of personal data (PII) such as emails, phone numbers, or payment card numbers. Results are stored in Amazon DynamoDB (metadata for comparison) and Amazon S3 (full results), which makes it possible to compare a new agent version with an earlier baseline version and detect regressions.
According to AWS, the system was tested by teams from 12 organizations in telecommunications, healthcare, and financial services. Executable notebooks are available for the supported frameworks, serving as a guide for selecting metrics and preparing datasets.
The source article is incomplete — it does not contain details about the license, availability outside the AWS environment, or the specific deployment method beyond what is described above. See the source article for details.
Why it matters
Until now, teams had to build evaluation metrics separately for each framework on which they run their agent, and rebuild them when switching to a different framework. By normalizing outputs into a single format, UAEF makes it possible to compare agent versions across frameworks on the same scale and detect regressions before the agent is deployed into production — which reduces the risk that a change to a prompt or model quietly degrades response quality or safety.
Two audiences, two different impacts
What this means
For individuals
Developers of AI agents get a unified way to measure quality (36 metrics across 7 dimensions) without having to build their own evaluation logic separately for each framework, making it easier to assess whether a change to a prompt, model, or agent tools actually improved things.
For a business
Companies developing AI agents on different frameworks can replace their own, framework-specific evaluation tools with a single unified system, which reduces the cost of maintaining evaluation and makes it possible to compare agent versions and detect regressions before release.
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