Xbench is a benchmarking platform designed to evaluate and track the productivity of AI agents across various domains. By utilizing live, expert-defined tasks from commercially significant fields, Xbench assesses an agent's ability to deliver tangible business value. Initial implementations include benchmarks for the recruitment domain, evaluating agents' effectiveness in talent sourcing, and for marketing, assessing the ability to identify suitable influencers for real-world campaigns. Xbench is designed as a continuously updated system that uses Item Response Theory (IRT) to track true capability growth over time. The platform provides a clear, value-oriented framework for guiding and predicting the development of effective, domain-specific AI agents. Key Features and Functionality: - Domain-Specific Benchmarks: Offers tailored evaluations for various industries, such as recruitment and marketing, to measure AI agents' performance in real-world tasks. - Continuous Updates: Employs a dynamic system that regularly updates benchmarks to reflect the evolving nature of AI agents and their environments. - Item Response Theory (IRT): Utilizes IRT to accurately track and measure the growth of an agent's capabilities over time. - Baseline Establishment: Provides baseline results for leading contemporary agents, facilitating comparative analysis and performance tracking. Primary Value and Problem Solved: Xbench addresses the need for a standardized, objective framework to evaluate and monitor the productivity of AI agents in specific domains. By offering continuous, real-world task assessments, it enables organizations to identify strengths and areas for improvement in their AI systems, ensuring they deliver tangible business value. This approach aids in guiding the development of effective, domain-specific AI agents and predicting their future performance trajectories.