HARDER, REAL‑WORLD BENCHMARKS LAND FOR CODING AGENTS
Terminal-Bench 2.0 and new SWE-Bench variants push coding-agent evaluation toward harder, real-world tasks. The updated [Terminal-Bench 2.0](https://huggingfac...
Terminal-Bench 2.0 and new SWE-Bench variants push coding-agent evaluation toward harder, real-world tasks.
The updated Terminal-Bench 2.0 ships 89 difficult CLI tasks with full tests and a public harness, showing frontier agents still miss many steps (docs at tbench.ai).
SWE-Bench Pro and Ramp SWE-Bench surface production-grounded GitHub issues and domain-specific work, pressuring agents to handle real repo state, tools, and long horizons.
Snorkel argues we’re under-measuring agents and is funding open benchmarks post; pair that with better agent prompt engineering to lift scores without overfitting.
Benchmarks are shifting from toy tasks to full environments, exposing reliability gaps before agents touch your repos.
Procurement and architecture should follow measured pass rates on realistic suites, not chat demos.
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terminal
Run your agent on a small slice of Terminal-Bench 2.0 with containerized tools; track pass@1, loop length, and tool error rate.
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terminal
A/B system prompts using the five agent levers; measure pass@1 delta on SWE-Bench-style tasks without model changes.
Legacy codebase integration strategies...
- 01.
Gate production automation behind a minimum score on Terminal-Bench/SWE-Bench; add rollback and dry-run by default.
- 02.
Instrument tool calls and loop guardrails; disable destructive actions until evals show stable behavior.
Fresh architecture paradigms...
- 01.
Bake an eval harness (Terminal-Bench + SWE-Bench) into CI from day one and promote models by score, not hype.
- 02.
Model tool contracts as prompts; keep environment snapshots to make failures reproducible.
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