Evaluating Coding Agents with Terminal-Bench 2.0 (snorkel.ai)

Snorkel AI announced it’s a major collaborator on Terminal-Bench 2.0, the evolving benchmark from Stanford and the Laude Institute that measures AI agents’ ability to perform complex, real-world tasks inside command-line environments. Terminal-Bench has become an industry standard because it evaluates end-to-end workflows—not isolated code snippets—mirroring the kinds of multi-step engineering tasks that can take humans hours or days. Its growing adoption (wide community contributions, GitHub traction, and inclusion on model cards like DeepSeek-V3.1-Terminus, Qwen3-Coder, and Claude Sonnet 4.5) makes its metrics increasingly influential for labs building coding assistants. Technically, Terminal-Bench is a suite of hand-crafted, human-verified tasks packaged in dedicated Docker environments, each with canonical solutions and test cases covering scientific workflows, networking, cybersecurity, build systems, and data pipelines. Results expose real weaknesses: OpenAI’s Codex (gpt-5-codex) scores 42.8% verified, struggling with chaining commands, reasoning over long outputs, and safe execution (the “rm -rf ~” risk). Snorkel’s role is to help calibrate task difficulty, contribute expert-verified datasets, and deepen performance analysis, pushing the benchmark toward more rigorous, production-relevant evaluations that better differentiate capable agentic systems from brittle assistants.