I came across a piece from WorkOS on how they test their AI agents. They built eval datasets, scoring systems, regression checks, and comparison tooling. Because nothing existed to do it for them. That tells you something important. This isn’t just a tooling problem. It’s an infrastructure gap.
Even teams building AI into their products don’t fully trust the outputs inside their systems.
I came across a piece from WorkOS on how they test their AI agents. They built evaluation datasets, scoring systems, regression checks and comparison tooling.
Because nothing existed to do it for them.
That tells you something important. This isn’t just a tooling problem. It’s an infrastructure gap.
We’re starting to see this show up inside real systems — where outputs become less consistent over time, even when nothing else has changed.
👉 Did this work? Most teams probably stop there.
👉 Is it still working? Almost nobody can answer that.
That’s where systems start to become unreliable. Not with errors. With drift. Outputs change. Quality slips. Nobody notices. Until it shows up in business results.
• lower conversion
• weaker decisions
• more manual workarounds
That’s the difference between testing AI… and keeping a system stable over time.
We’ve spent years fixing financial systems where trust in the outputs has eroded.
AI is starting to introduce the same pattern, but it is harder to see.
Most teams aren’t even measuring it yet.