A token is, technically, a unit of text: every word or fragment sent to a model and every word it returns. Vendors bill by the token. In practice, a token bill that has grown out of control measures something else, which is how little a company understood a process before it decided to automate it.
Responsibility, defined
The dictionary definition is useful here. Responsibility is the state of having a duty to deal with something, or having control over it. A duty is a moral or legal obligation.
So to be responsible for something is to consider that you have an obligation toward it and that you have control over it. That is not a burden. It is the condition under which a thing can be kept in working order. A person responsible for a vehicle keeps it maintained. A person responsible for equipment keeps it operational. An accountant responsible for the books keeps them reconciled.
Responsibility is often confused with blame, which is the state of being held accountable for something that has already gone wrong. That confusion is itself a symptom. Either the area is in order and someone is responsible for it, or it is not, and the search for a scapegoat has begun.
How to recognize an irresponsible area
A person who is not responsible for an area avoids it. An experienced manager knows the signs: the meetings that are missed, the reports that arrive late, the tasks performed mechanically because they are required rather than owned. Any organization has such areas, and there is a reliable way to locate them. Where there is a great deal of activity and very little output, there is a concentration of irresponsibility.
Hiring is the clearest contemporary example. On one side, employers deploy automated screening because nobody wants to read the applications. On the other, candidates use automated tools to produce applications that pass the screening. The result is machines on both sides generating enormous activity while very few people are hired. Nobody in the chain is responsible for the outcome, and the outcome shows it.
The token bill
Now apply this to AI agents: systems that are given a goal, tools, and a budget, and set loose on a task. Where the task is well understood, this is useful. Where it is not, the pattern is predictable.
A process is not working. Nobody examines why. An agent is placed on top of it with instructions to handle it. The process continues not to work, exactly as before, and a new cost is added, because the agent is diligent. It will do anything to complete the task it was given, and the cheapest way for an agent to complete an ill-defined task is to keep trying. The bill for that is measured in tokens.
The rule is simple. A field of irresponsibility cannot be repaired by adding more irresponsibility to it. If a hiring process costs far more per hire than it should and nobody can explain why, an agent will not reduce the cost. It will add its own cost on top.
The characteristic outcome is a manager who is pleased with an automation that sends a polite acknowledgment to every incoming application at a substantial monthly cost, while the recruiting the company actually asked for has still not happened. The automation is a response to the wrong problem, produced by someone who was avoiding the right one.
The order of operations
There is a sequence that works, and it does not start with the model.
Stop the process. When the warning light comes on, pull over. A process that produces no output should not continue running, and certainly should not be accelerated.
Study it. If you are responsible for the area, go in and find out what is wrong. Engineers absorbed in side projects. A finance team that replaced software that worked for a decade and now cannot take a payment. An invoicing department replaced by a single agent that has no idea how to accept one. All of these are real, and all of them are visible to anyone who looks.
Correct it, and record what happened. Find the specific failure, have the responsible person fix it, and write down what went wrong and how the organization will avoid it. If the responsible person was you, that applies equally.
Then automate. Once the process produces its output reliably, a model layered on top of it can make it faster and cheaper. Automation multiplies whatever is already there. On a working process it multiplies output. On a broken one it multiplies cost.
A budget is a statement of understanding
My own AI spend is a fixed monthly amount, and it is enough to run a great deal of work. That is possible because I know what I ran and why, I can estimate each task in advance, and when something goes wrong I do not retry until the bill grows. I stop, find the cause, and correct it before the model touches the task again.
That is the whole principle of AI cost engineering as we practice it. A cost ceiling is not a constraint imposed on a working system. It is evidence that the system is understood. When a company cannot say what its AI spend will be next month, the problem is rarely the price of tokens. It is that nobody is responsible for the process the tokens are being spent on.