Terminal‑Bench 2.0 marks a major step forward for AI‑agent evaluation in developer‑style terminal environments. The new suite contains 89 tasks that have undergone several hours of manual and LLM‑assisted validation, ensuring each scenario is solvable, realistic, and clearly specified. By removing fragile dependencies—such as the earlier download‑youtube task that relied on unstable third‑party APIs—researchers now have a more reliable benchmark that raises the difficulty ceiling while improving reproducibility. Despite the higher bar, top performers like OpenAI’s Codex CLI (GPT‑5) maintain a near‑50 % success rate, underscoring the tight competition across leading models and the need for continued refinement.
Complementing the benchmark, Harbor provides the infrastructure to scale agent rollouts in cloud‑deployed containers. The framework is agnostic to agent architecture, supporting any container‑installable model and enabling large‑scale supervised fine‑tuning (SFT) and reinforcement learning (RL) pipelines. Harbor’s compatibility with providers such as Daytona and Modal means that developers can orchestrate tens of thousands of experiments effortlessly, integrating custom benchmarks and automatically submitting results to the public leaderboard. Internal use of Harbor during the benchmark’s creation demonstrates its robustness, and its public release at harborframework.com opens the door for community contributions and standardization. Together, Terminal‑Bench 2.0 and Harbor lay the groundwork for a unified, reproducible evaluation stack that can accelerate the development of autonomous agents in realistic operational settings.
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