#agent-evals
4 articles
Benchmark Fatigue Is Real, and the Harness Is the Cure
Leaderboards move every few weeks and teams are tired of re-evaluating their stack every time a new model tops one. The teams shipping reliably have mostly stopped chasing benchmark scores and started investing in the evaluation harness that tells them how a model performs on their actual task.
The Benchmark Is Not the Product: Why Agent Evals Must Become Executable Specifications
Agent benchmarks can tell you whether a system cleared an obstacle course. They cannot tell you whether it will behave sensibly inside your company—unless evaluation becomes part of the product’s architecture.
The Benchmark Passed. The Software Still Broke.
Coding-agent evaluations are getting harder, but a larger test suite is not the same as a better measurement. The next useful benchmark will judge recovery, restraint, and maintainability—not merely whether a patch turns the checks green.
The Hard Part of Agentic AI Is Measurement, Not Reasoning
The industry still talks as if smarter models automatically become better agents. The harder truth is that once models can act, the bottleneck shifts to observing, scoring, and constraining behavior in the messy conditions where real work happens.