AI agent developers lack systematic methods to validate and benchmark agent capabilities before deployment

AI engineers building autonomous agents struggle to objectively evaluate whether their agents actually possess the skills they claim to have, leading to unreliable deployments and wasted resources. Current approaches rely on ad-hoc testing, reading documentation, and trial-and-error, which creates uncertainty about agent performance and prevents confident production use. Teams need automated, systematic evaluation frameworks that can measure agent skills consistently and catch capability gaps before agents fail in real-world scenarios.