UAEF: A framework-agnostic system for closed-loop evaluation of AI agents
The Universal Agent Evaluation Framework (UAEF) is a framework-agnostic system for evaluating AI agents. UAEF converts each framework's traces into one canonical format, scores them with 36 built-in metrics across 7 dimensions, and stores results so teams can compare agent versions and catch regressions.
The Universal Agent Evaluation Framework (UAEF) is a system designed to evaluate AI agents without being tied to a specific framework. Many teams use different frameworks like LangGraph, LangChain, Strands Agents, and Amazon Bedrock AgentCore to build their AI agents. Each framework has its own way of recording agent execution, making evaluation tooling difficult to use consistently across frameworks.
UAEF addresses this issue by providing a framework-agnostic evaluation system that can work with traces from any supported framework.
To use UAEF, teams provide the agent they want to evaluate and a dataset to run it on. The dataset includes inputs for each test case, and can also contain ground-truth answers for comparison. UAEF takes two inputs: the agent under test and the dataset. It then scores the agent's performance across 36 built-in metrics in 7 quality dimensions. These dimensions include tool calling, response quality, responsible AI, performance, multi-turn conversation, multi-agent coordination, and reasoning.
UAEF first converts the agent's native execution into a common format called the AgentTrace. This normalization allows for consistent scoring regardless of the framework used. The evaluation engine then screens the trace for personally identifiable information (PII) and scores the trace based on the selected metrics. The results are stored in Amazon DynamoDB and Amazon S3 for comparison and reporting purposes.
Teams can then compare agent versions before release, detect regressions, and generate reports and insights to inform the next iteration of the agent.
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