Most SaaS teams ship agents without a way to check their work. The hard part isn’t the model, it’s knowing when “done” actually means done.
The story
*Patterns making it read AI-ish:*
– Stat opener feels unsourced and tidy.
– Repeated “X isn’t Y, it’s Z” structure.
– Buzzwords: “architecture problem,” “non-deterministic outputs,” “instrumented evaluation.”
– Many punchy aphorisms in a row, not much messy human texture.
– No concrete example of an agent messing up.
– Binary framing: feature add-on vs infrastructure decision.
*Rewrite:*
Most SaaS teams say they’re exploring agentic AI. Far fewer have a plan for the moment an agent says, “Done,” while confidently doing the wrong thing for every customer. Yay.
I’m seeing a wiring problem, not a strategy problem. And the pile is growing while everyone admires the shiny robot.
*Agent mistakes stack.* One step being right most of the time sounds nice. Chain ten choices together and your final answer starts wobbling around on baby giraffe legs.
*Most SaaS products expect failures to be boring.* A button breaks. A field is missing. Agents don’t offer the courtesy of being boring. They can sound calm, clean, and certain while wandering into a ditch.
*The teams shipping dependable agent products faster* aren’t winning because they found the wizard model. They’re winning because they built ways to check the work before giving the agent more freedom. Boring? Yes. Helpful? Also yes.
Builders treating agents like a feature add-on are playing a different game than builders treating them like product plumbing. The gap shows up later: in production, with paying customers watching. Small party foul.
The question isn’t whether to build with agents. It’s whether your product can tell the difference between an agent finishing a task and finishing it correctly.
Where does your product rely on human judgment right now? Where is an agent about to take the wheel?
I’ve been thinking about this in a larger thread I’m calling *The Extinction of Software*. Agents don’t only change how
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