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 τ-bench and friends do and don't tell you
How to read agent benchmarks like τ-bench, MCP-Bench and AgentBench — what they measure, where they mislead, and why your own evals still matter most.
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: calibrate it before you trust it
How to make an LLM judge reliable — aligning it to human labels, killing position and verbosity bias, and knowing when a judge is measuring quality versus its own preferences.
Read →Ship agents you can trust
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