Reliability, Evals & Quality
LLM-as-a-judge done right, agent benchmarks, regression testing, and CI/CD quality gates that keep agents from breaking in production.
Articles
Evaluating AI agents: from evals to reliable production
How to evaluate autonomous agents you can trust — LLM-as-a-judge done right, the metrics that matter, and the CI/CD quality gates that keep agents from regressing.
Read →Agent benchmarks: what SWE-bench, τ-bench and GAIA actually measure
The agent benchmarks worth knowing — SWE-bench Verified, τ-bench with its pass^k reliability metric, GAIA's human-vs-model gap — where leaderboard numbers mislead, and a reading checklist for the next score a vendor shows you.
Read →Regression testing for AI agents: stop fixing one thing and breaking three
Why a golden dataset and CI gates are the only reliable defense against silent agent regressions when models, prompts and tools change underneath you.
Read →LLM-as-a-judge: prompts, code and calibration that make it trustworthy
A working LLM-as-a-judge setup: a judge prompt template you can copy, a DeepEval G-Eval example, the three biases Zheng et al. measured and how to counter them, calibration against human labels, and the cases where a deterministic check beats a judge.
Read →Ship agents you can trust
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