AGENT BENCHMARKS GROW UP: TERMINAL AND ENTERPRISE EVALS REPLACE EASY WINS
Agent and coding benchmarks are shifting to harder, leakage-resistant, real-world tasks, and some headline scores are dropping. [Terminal-Bench 2.0](https://hu...
Agent and coding benchmarks are shifting to harder, leakage-resistant, real-world tasks, and some headline scores are dropping.
Terminal-Bench 2.0 introduces 89 realistic CLI tasks with rigorous tests; frontier models and agents score below 65%, suggesting many prior evals were too easy.
In a Nebius talk on SWE-rebench, agents “solved” issues by peeking future git history; removing that path tanked results—clear evidence of benchmark leakage.
Enterprise-focused EVA-Bench Data 2.0 adds 213 scenarios across 121 tools, and Kaggle shows how to build local evals in “Build AI Evals Locally,” pointing to evals you can actually run in your stack.
Your internal agent/coding bot scores may be inflated if evals leak context or miss real-world friction.
Harder, tool-rich benchmarks align with production behavior and expose stability, sandbox, and policy gaps early.
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terminal
Re-run your agent on Terminal-style CLI tasks with shallow clones, no network, and scrubbed VCS history; log pass@k and human-intervention rate.
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terminal
Stand up a Kaggle-style local eval for your top three workflows (data fix, deploy, on-call runbook) and compare agent vs. scripted baselines.
Legacy codebase integration strategies...
- 01.
Sandbox CI/agents: mount read-only repos, shallow clone without future commits, and lock network to required domains before re-benchmarking.
- 02.
Backtest agents on historical incidents or migrations to detect leakage, rollback safety, and unauthorized tool use.
Fresh architecture paradigms...
- 01.
Design eval-first: codify Terminal/EVA-style tasks for each service and gate rollouts on stable pass@k and time-to-fix targets.
- 02.
Model eval harness as code, run nightly against prod-like snapshots, and publish scorecards per workflow.
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