ARA Eval

AI agents are making decisions on behalf of your organization. ARA Eval tells you which ones catch the risk gates that matter — and which ones miss them.

The cost of building software is collapsing. Tools like OpenClaw generate entire applications from prompts. A 70-person manufacturer replaced its entire SaaS stack with AI-generated apps consuming 250 million tokens a day. As Forrester's Frederic Giron observed in “When Code Is Free, What's Left to Sell?” , AI-assisted builds are proliferating faster than governance frameworks can keep up. “Code that no one wrote by hand is code that no one fully understands.”

Enterprises feel pressure to deploy agent systems but have no structured way to assess the risk — and most don't know where to start. Nobody has answered the basic question: how do you evaluate whether an AI agent's judgment is safe enough to act on?

ARA Eval answers it. We test AI agents against 6 enterprise scenarios with human-authored reference fingerprints, scoring them on whether they catch the gates that matter. The methodology is open source and reproducible — run it yourself. The leaderboard makes the results transparent: which models catch dangerous risk gates, which ones miss them, and what the practical implications are for deployment decisions. You can try the eval live or run the framework yourself.