ClawCheck is a practical evaluation framework and prototype designed to help teams test the safety, reliability, and ethical behavior of AI agents.

As AI agents become more capable, it becomes important to understand not only whether they can complete tasks, but whether they can respond responsibly, identify risks, admit uncertainty, avoid hallucinations, and provide useful recommendations. ClawCheck is built around this need.

The project works as an AI agent assessment workflow. A user selects or creates a test prompt, runs it on a target AI agent, collects the response, and then evaluates that response through ClawCheck’s structured scoring framework. The evaluation looks at key areas such as risk identification, privacy concerns, bias and fairness, misuse potential, hallucination, human oversight, stakeholder awareness, and confidence quality.

Our goal is to produce both technical and non-technical artifacts for the sprint. The technical side may include a simple prototype or workflow that allows users to enter an agent response and generate an evaluation report. The non-technical side includes a red-team taxonomy, scoring rubric, test case library, documentation, and sample evaluation reports.

ClawCheck is designed to be accessible. It can work even when the target AI agent does not have API access, because users can manually paste the agent’s response for evaluation. This makes it useful for hackathon teams, researchers, startups, and community members who want a lightweight way to assess agent behavior.

For the BGI Sprint, ClawCheck contributes to the broader goal of making Beneficial General Intelligence more testable, understandable, and actionable. It supports safer experimentation with AI agents by turning abstract safety concerns into concrete evaluation criteria, repeatable test cases, and clear improvement recommendations.