This article discusses Agent Bricks, a platform that creates AI agents tailored to specific business data and tasks. It covers how to improve the accuracy of these agents through automated evaluations and human feedback, along with practical insights on deploying AI in organizations.

AIRS-Bench evaluates the research capabilities of large language model agents across 20 tasks in machine learning. Each task includes a problem, dataset, metric, and state-of-the-art value, allowing for performance comparison among various agent configurations. The framework supports contributions from the AI research community for further development.