A paper proposes a new approach to finding high-quality test suites for self-evolving AI agents. The method, called TeSearch, uses a quality-diversity evolutionary algorithm to search for small and strong test suites that can effectively evaluate the agent's behavior. This approach is shown to outperform other methods in terms of suite quality and size, and is used as an improvement signal to reject 59.9% of behavior-changing rewrites.