The Merge: Your tests pass. Can your agent finish the job?

Cua AI develops environments that enable AI agents to operate computers and evaluates them through separate testing layers for control reliability, task completion, and grading accuracy. CEO Francesco Bonacci explained that a successful click or drag does not prove an agent chose the correct action or completed the broader assignment, making independent verification of application changes essential. Cua-Bench evaluates agents using tasks with defined starting states, reference solutions, and software evaluators that inspect saved files or application states, including circuit-design assignments in KiCad where seven tested models completed no more than six of 25 tasks under a 200-step limit. To validate the evaluators themselves, Cua uses adversarial reviews in which one agent follows a task and another intentionally violates it, with reviewers assessing whether the grader correctly distinguishes success from failure. The discussion also emphasizes isolated environments, security reviews, and human oversight as important safeguards when agents access tools, sensitive data, and computer systems, particularly as developers increasingly delegate longer-running work to autonomous agents.

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