An End-to-End Agent Auditing Engine

With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains. The fast-evolving harness ecosystem has also made rigorous capability evaluation increasingly important. However, efficiently building an end-to-end, systematic, and comprehensive evaluation pipeline remains a significant challenge. To address this challenge, we introduce $A^2E$ (Agent Auditing Engine), an end-to-end evaluation engine des

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ethics.ai (7 August 2026), “An End-to-End Agent Auditing Engine,” evidence record 17921, https://ethics.ai/record/17921 (originally published by arXiv cs.AI).

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