Artificial Intelligence1 min read

UAEF: A framework-agnostic system for closed-loop evaluation of AI agents

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In short

The Universal Agent Evaluation Framework normalizes diverse execution traces into a single format named AgentTrace using specialized adapters. It evaluates performance across seven core dimensions, including reasoning, responsible AI, and multi-agent coordination, utilizing 36 built-in metrics. To safeguard sensitive data during benchmarking, the system scrubs personally identifiable information like phone numbers and credit cards before saving the final records.

Key points

  • Teams can measure prompt, model, or tool adjustments within a closed-loop cycle without r…: Teams can measure prompt, model, or tool adjustments within a closed-loop cycle without rewriting their core evaluation logic.
  • Sensitive execution trace data is automatically scrubbed of personally identifiable infor…: Sensitive execution trace data is automatically scrubbed of personally identifiable information before storage in Amazon S3 and DynamoDB.
  • Benchmarking requires only an agent and a dataset, leaving ground-truth answers and tool…: Benchmarking requires only an agent and a dataset, leaving ground-truth answers and tool expectations as optional inputs.

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Artificial Intelligence

UAEF: A framework-agnostic system for closed-loop evaluation of AI agents

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…

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