The developers of Terminal-Bench, a benchmark suite for evaluating the performance of autonomous AI agents on real-world terminal-based tasks, have released version 2.0 alongside Harbor, a new framework for testing, improving and optimizing AI agents in containerized environments. The dual release aims to address long-standing pain points in testing and optimizing AI agents, particularly those built to operate autonomously in realistic developer environments.With a more difficult and rigorously verified task set, Terminal-Bench 2.0 replaces version 1.0 as the standard for assessing frontier model capabilities. Harbor, the accompanying runtime framework, enables developers and researchers to scale evaluations across thousands of cloud containers and integrates with both open-source and proprietary agents and training pipelines.“Harbor is the package we wish we had had while making Terminal-Bench,” wrote co-creator Alex Shaw on X. “It’s for agent, model, and benchmark developers and researchers who want to evaluate and improve agents and models.”Higher Bar, Cleaner DataTerminal-Bench 1.0 saw rapid adoption after its release in May 2025, becoming a default benchmark for evaluating agent performance across the field of AI-powered agents operating in developer-style terminal environments. These agents interact with systems through the command line, mimicking how developers work behind the scenes of the graphical user interface.However, its broad scope came with inconsistencies. Several tasks were identified by the community as poorly specified or unstable due to external service changes.Version 2.0 addresses those issues directly. The updated suite includes 89 tasks, each subjected to several hours of manual and LLM-assisted validation. The emphasis is on making tasks solvable, realistic, and clearly specified, raising the difficulty ceiling while improving reliability and reproducibility.A notable exa …
