UAEF: A framework-agnostic system for closed-loop evaluation of AI agents
Machine Learning Blog
This article introduces the Universal Agent Evaluation Framework (UAEF), a framework-agnostic system for evaluating AI agents built on different platforms with consistent metrics.
- Adapters convert agent traces from LangGraph, LangChain, Strands Agents, and Amazon Bedrock AgentCore into a canonical format
- 36 built-in metrics across 7 dimensions: tool calling, response quality, responsible AI, performance, multi-turn, multi-agent, and reasoning
- Stores experiment results in Amazon DynamoDB and S3 for comparison and regression detection across agent versions
- Provides web UI and Python library for evaluation, with LLM-generated insights to guide prompt, model, and tool improvements
- Includes PII detection, statistical reporting, and support for single-turn, multi-turn, and multi-agent scenarios
- Open source under Apache 2.0 with runnable notebooks for each supported framework
UAEF enables teams to evaluate and compare AI agents consistently across frameworks, closing the loop between evaluation results and iterative improvements.
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