An AI Agent in Every Step Won't Tell You Which Steps Were Worth Taking

When the iPhone and the App Store became first became wildly popular, everyone walked around saying, “there’s an app for that!” And we all thought we were pretty clever. Well, it’s the Age of AI ™ now and instead of apps, now it’s, “there’s an agent for that!” Everything gets an agent to make your life easier, faster and more productive. Really?

Product management as a discipline isn’t immune from this. Far from it. The pitch making the rounds right now is "AI agents across the entire product lifecycle." An agent for research. An agent for prioritization. An agent for execution. An agent for the retro. It makes for a great demo. It's also an answer to a challenge almost nobody is struggling with.

Teams are not slow at producing outputs. They never were. Putting an agent in every step of the lifecycle makes an already-fast factory faster. But speed was never the bottleneck. The bottleneck is increasingly making the right judgment calls and deciding which of those outputs were worth producing in the first place.

Automating the production of work you never should have done in the first place doesn't save you anything. It gets you to the wrong place faster, with more confidence and a tidier audit trail. An agent that writes a flawless PRD for a feature no customer wanted hasn't helped you. It's helped you waste time more efficiently.

The product lifecycle isn't a conveyor belt that needs more horsepower at each station. It's a series of decisions. Should we build this? For whom? What behavior are we trying to change? How will we know if it worked? Those are the expensive questions, and they're exactly the ones an agent embedded in a workflow step is least equipped to answer. Why? Because the answer doesn't live in the workflow. It lives in the outcome the work is supposed to produce.

This is the distinction we keep returning to with the teams we work with. An output is a thing you ship. An outcome is a measurable change in human behavior that creates value. Agents are extraordinary at multiplying outputs. They're useless at telling you whether the output changed anyone's behavior. That part is still yours.

None of this is an argument against the tools. We use them. Our clients use them. Pointed at the right target, an agent is a genuine accelerant. Pointed at the wrong one, it's a liability with great production values. The teams getting real value from AI right now aren't the ones who can say, before the work starts, what outcome a feature is meant to move and how they'll measure it.

So before you wire an agent into every step of your lifecycle, run one cheaper experiment. Take last quarter's roadmap and, for each item, write down the behavior change it was supposed to create and whether it actually did. If you can't name the outcome, no agent in the world will save that item. It'll just produce it faster.

That's the work that AI doesn't automate. It's also the work that decides whether all that speed was worth anything.