Evaluating AI Agents in Production

The field had been quietly avoiding a question, and the WebVoyager paper was the first to say it out loud. Published April 1st, it wasn't a breakthrough announcement — it was a diagnosis. Web-based AI agents were already running in real environments while the methods for evaluating them remained borrowed from static benchmarks built for a different era, designed to measure systems that don't browse, don't click, don't encounter the unpredictable texture of the live web. The paper's argument was specific and uncomfortable: consistency and transparency in agent evaluation weren't problems waiting for more compute. They were methodological failures hiding in plain sight. Then, five weeks later on May 6th, a second paper arrived to extend the wound. "Evaluating Agentic AI in the Wild" moved the conversation from framework design to production reality — cataloguing failure modes that only appear when agents operate continuously in deployment, drift patterns that accumulate slowly enough to escape notice until something breaks, and sketching a framework for evaluation that could actually survive contact with the messy, non-stationary environments these systems now inhabit. Two papers, two months, one gathering argument: the industry shipped the agents before it built the instruments to understand them.

If confirmed, here is what this means. Every organisation currently running agentic AI in production — in customer service, research automation, workflow orchestration — is operating with evaluation infrastructure that may be measuring the wrong things, or measuring nothing useful at all. The drift patterns identified in the May paper are particularly consequential: an agent that performs well at deployment can degrade gradually in ways that standard monitoring never catches, accumulating small failures until a threshold is crossed. That's not a theoretical risk — it's the kind of failure that looks, from the outside, like a sudden collapse. The second-order effect is strategic: companies that build robust production evaluation frameworks now will have a structural advantage over those still relying on pre-deployment benchmarks, not because better evaluation makes agents smarter, but because it makes their failures visible early enough to fix.

Watch for either of these preprints clearing peer review at a major venue, or for a practitioner-facing implementation — a tool, an open benchmark, a production case study — that translates the framework into something engineers can actually deploy.

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