$A^2E$ : An End-to-End Agent Auditing Engine
Quick summary
arXiv:2608.07346v4 Announce Type: replace Abstract: 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 designed for agent harnesses. $A^2E$ leverages our
Key takeaways
- arXiv:2608.07346v4 Announce Type: replace Abstract: 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.
Why it matters
AI progress is not only a software story. Chips, data centers and energy decisions help determine which models can operate economically and what end users ultimately pay.

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