What Do You Actually Want? | dekodiert

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An agent is supposed to win a boat race. It gets rewarded for collecting green blocks. So it drives in circles, racks up points, and never finishes the race. The score looks excellent. The purpose is missed.

That sounds like a neat lab anecdote from AI research. In practice it is also a fairly precise description of many corporate AI initiatives. The dashboards look good. Activity goes up. Cost per task goes down. The number of generated artifacts rises. And yet it becomes less clear, not more, whether the system is optimizing for the right thing.

At that point the problem often is not the model. The problem is that the organization itself cannot clearly say what it actually wants.

Right now we spend a lot of time talking about prompts, specs, context windows, agent setups, and evaluation. All of that matters. But much of it assumes something that, in a surprising number of organizations, exists mainly as a polite fiction: an articulable intent.

Not the mission-statement version of intent. Not the website version. Not what sits in the town hall deck. I mean the answer to a few simple questions that can survive contact with reality:

What are we actually trying to achieve here?

Which trade-offs really apply?

Which sacrifices are quietly acceptable?

How would we notice that the initiative is becoming more efficient while moving in the wrong direction?

If the honest answer to those questions turns evasive, contradictory, or politically unsayable, you do not have a prompt problem. You have an intent problem.

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The problem sits one layer higher

Many companies still treat AI as if it were mainly a specification problem. People need to formulate more precisely. Briefs need to improve. Data needs to be cleaner. Use cases need to be sharper. Governance needs to be stronger. Often that is true.

It is just not the first question.

Before you can specify well, you have to know what is actually worth specifying. Before you can build serious evaluation, you have to know what the result is supposed to be evaluated against. Before you can delegate meaningfully, you have to know which intent should survive the handoff once the original plan breaks against operational reality.

That is where a surprising number of organizations go soft very quickly.

Then you hear sentences like:

We want to use AI to become more productive.

We want to become more innovative.

We want to relieve our employees.

We want to improve our cost base.

We want to stay ahead.

None of those statements is false. Almost all of them are too vague to function as intent, especially when they are supposed to be true at the same time.

Because in practice they collide. Anyone who wants to cut costs does not automatically reduce pressure on employees. Anyone who wants to accelerate innovation does not automatically reduce risk. Anyone who wants higher productivity does not automatically get better quality. And anyone who wants to stay ahead still has not said what they would actually give up in order to mean it.

An organization without articulable intent does not write a good spec. It writes a tidy confusion.

Practical test: If you want to test the argument against your own initiative: the three templates for this essay guide you through goal clarification, contradiction checks, and delegation readiness. You do not get a finished strategy or a tool recommendation, but better questions for the next decision.

Three old answers to the same problem

The interesting thing about the intent question is that it is not new. What is new is that AI makes it brutally visible.

Three very different traditions land on the same structure.

Mission command

Prussian mission command did not emerge because someone wanted leadership to sound more poetic. It emerged because people understood that plans collapse under complex conditions. Anyone who knows only the order fails the moment reality departs from the script. Anyone who understands the purpose can adapt locally without losing direction.

The key point is not decentralization as ideology. The key point is this: the person acting has to understand the purpose, not just the instruction.

That is very close to the problem that now appears in AI delegation as well. If an agent, a team, or a business unit is supposed to act with some autonomy, it is not enough to prescribe isolated steps. The intent has to be clear enough to hold once the first plan stops fitting.

Hoshin Kanri

In the West, Hoshin Kanri often gets reduced to a method for cleaner cascades of goals. That is the harmless version. The real demand is tougher.

The system only works if a company can translate strategic priorities in a way that lower levels do not experience merely as reporting duty, but as usable direction. It fails very reliably when that translation does not happen.

Then the whole architecture...