#testing
5 articles
AI Code Needs a Chain of Custody, Not a Vibe Check
The dangerous question is no longer whether generated code looks plausible. Engineering teams need to know what evidence justifies every change and who owns the uncertainty that remains.
The Benchmark Is Part of the Model Now
Public leaderboards increasingly measure how well an AI system has adapted to a test ecosystem, not merely whether it can solve the underlying task. Serious evaluation must become a living engineering practice rather than a quarterly scorecard.
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.
Benchmarks Are Not Enough: Why AI Evaluation Must Start Looking Like Systems Engineering
The AI field still treats evaluation as a leaderboard problem. That made sense when models mostly answered questions; it makes far less sense when they plan, call tools, and operate inside messy workflows.
The Next AI Bottleneck Is Not Model Size. It Is Evaluation Drift.
For two years, the industry treated benchmark gains like a scoreboard for intelligence. The harder problem is now visible inside real deployments: models change, tasks change, and the evaluation frame quietly stops matching the work.