Coursera Flash Sale
40% Off Coursera Plus for 3 Months!
Grab it
Learn about Terminal-Bench, a groundbreaking benchmark for evaluating large language model agents in realistic terminal environments, presented in this 31-minute conference talk from Ray Summit 2025. Discover how Stanford researcher Mike Merrill addresses critical gaps in current agent evaluation methods by introducing a challenging, real-world-grounded benchmark that meaningfully measures progress toward autonomous, long-horizon AI agents. Explore the limitations of existing benchmarks that either fail to reflect real-world tasks or are too simplistic to differentiate frontier model capabilities, and understand how Terminal-Bench provides carefully curated challenging tasks within computer terminal environments inspired by actual engineering and operational workflows. Gain insights into the team's findings from Terminal-Bench progress and preview upcoming features in Terminal-Bench 2.0, including expanded task sets, enhanced environment dynamics, and sophisticated evaluation metrics designed to test reasoning, planning, and tool use capabilities. Examine the broader vision for unifying agent evaluation and training through a new open-source framework that enables reproducible, standardized, and scalable agent development, while understanding what it takes to evaluate real-world agentic capabilities and how Terminal-Bench is shaping the future of agent benchmarks and AI research.
