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EvaluationExperimental

A2E

by datamllab

Trace-based A2A evaluation and agent track record builder

Python
Updated Sep 12, 2026
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Overview

Analyzes agent interactions and traces to evaluate agent-to-agent behavior and failure modes. Collects trajectories, telemetry and trace data (OpenTelemetry) to compute evaluation signals and build an agent track record. Designed for post-run auditing and continuous evaluation with a focus on A2A metrics and trajectory analysis. This supports the Agent-to-Agent Protocol (A2A) and helps surface risk signals to reduce Inter-Agent Miscommunication in complex deployments.

The Value Proposition

As agents coordinate and delegate, simple correctness metrics no longer capture real-world risk — you need signals about how agents behave with each other. A2E centralizes interaction traces and telemetry so teams can measure agent reliability, surface failure modes, and create reproducible A2A evaluation artifacts. That visibility is essential for building Multi-Agent System trust and continuous agent evaluation pipelines.

Ideal For

Researchers and teams who need to audit multi-agent interactions, trace failures, and build agent-to-agent evaluation pipelines. For practical deployment patterns, consider the Agent Service Mesh Pattern to orchestrate and observe agent collaborations.

Use Cases

  • Capture and replay agent interaction trajectories for post-mortem analysis
  • Measure agent-to-agent reliability and surface common failure modes
  • Combine OpenTelemetry traces with trajectory data to build agent track records for governance
  • Integrate continuous evaluation into CI for pre-production agent testing
Works With
openinferenceopentelemetrypython
Topics
agent-evaluationagent-observabilityagentic-aiai-agentsauditingllm-evaluationllm-tracingopeninferenceopentelemetrypython+2 more
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Keywords
A2A evaluationagent track recordagent-observabilitymulti-agent trust