Universal Agent Evaluation Framework simplifies AI agent testing

Aws ML Blog2 hours ago

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Universal Agent Evaluation Framework simplifies AI agent testing

The Universal Agent Evaluation Framework (UAEF) provides a standardized system for testing AI agents regardless of the underlying development framework. By normalizing execution data into a common format, it allows teams to consistently measure performance and identify regressions across different agent versions.

Key points

UAEF uses adapters to convert execution traces from various frameworks, such as LangChain and Amazon Bedrock AgentCore, into a single canonical format called AgentTrace.
The system evaluates agents using 36 built-in metrics categorized into seven dimensions, including tool calling, response quality, responsible AI, performance, multi-turn conversation, multi-agent coordination, and reasoning.
Evaluation results are stored in Amazon DynamoDB and Amazon S3, enabling teams to compare current agent performance against baselines to detect improvements or regressions.
The framework includes automated PII redaction to protect sensitive data before storage and provides detailed reports that offer statistical insights and recommendations for agent iteration.
The system supports both deterministic metrics and LLM-as-a-judge metrics, though the documentation notes that judge models are treated as untrusted inputs to prevent agents from influencing their own scores.

This story was rewritten with AI assistance using only captured and verified information. Consult the original source for definitive details.

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