June 26, 2026

AI is Becoming an Executor

Assiduity AI

Governed Execution: Managing Agentic AI — Article 1 of 13

That was the simple time. The answers were satisfactory. The more difficult question came suddenly. It is no longer about whether the output is correct but whether anyone can say what really happened during the process of producing it.

Envision a project team getting ready for a stage-gate review. They give an AI agent a standard job: to verify the requirements, link each one to the approved source documents, identify any gaps, and then prepare a memo for the gate. Eventually, the memo is completed. It is well organised. The requirements have been mapped, the gaps have been noted, and the recommendation is reasonable. When the reviewer reads it, they see no errors.

Now look at the questions that the memo fails to answer. Did the agent remain within the approved documents or reach out to a plausible source outside that boundary? Did it follow the rule that any missing evidence must be noted as missing, or did it simply make an inference instead? And when it encountered a dependency that it couldn’t resolve, did it escalate the issue or instead decide on its own that the dependency didn’t matter?

The memo looks the same either way.

What changed? The agent no longer helps someone who remained in control of the work; it carried out the work itself. It searched, compared, decided what to include and exclude, and produced a result the organization will act on. A human set the task and will sign the memo. Nevertheless, the execution took place within the machine, including the actual series of significant actions. We call this action delegated machine execution: the assignment of multi-step, goal-directed work to a system that carries it out on behalf of the organization.

This brief phrase represents a material change. In management history, delegation meant giving work to someone who could be given instructions and held accountable. Action and accountability were linked through people, roles, contracts, and institutions.

Agentic AI pulls that connection apart.

The actual execution can now take place almost anywhere. It can operate on the infrastructure of a model provider through a series of specialized agents whose workings are opaque.

Even though execution is now mobile, accountability isn’t. If the memo is incorrect, then the regulator, the customer, the board, or the court will seek out a company and, among the employees of that company, identify an individual. They will not treat the model as being the responsible party since there is no real means of redress against it.

The division has always existed: while outsourcing shifted task execution to different parts of the world, accountability stayed in the same place. Agentic AI makes this division more distinct, faster to achieve and easier to overlook. This series is about the gap between where the work is now carried out and where responsibility still lies.

What actually changed?

It is tempting to regard this as just another instance of automation. In the past, we entrusted work to machines and the benefits eventually appeared, leading to a larger economy and changed workers’ roles. But this reassurance hides the most important point: the character of the work has changed.

Traditional automation functions because the judgment occurs in advance. A payroll system carries out the same calculation a million times. Its behaviour is unchanging, can be examined, and is boring. When it fails, the cause can generally be identified as a rule, an input, or a line of logic. The variables and process are fixed. The machine carries out calculations within narrow limits.

Agentic AI takes the opposite approach. It is introduced where the task’s scope is broad and not clearly defined. It exercises judgment through a series of steps and does not necessarily do so the same way each time. For people of a certain age, this is reminiscent of the Choose Your Own Adventure books. The story could follow different paths depending on choices made. Agentic AI thus raises a similar governance issue: the same task may not unfold the same way twice.

The difference lies in two aspects: choice and replication. Traditional automation usually doesn’t choose and is designed to replicate. Agentic AI can select its path, and in a probabilistic system it will not always follow the same path. It chooses without ensuring replication. This ability makes agentic AI useful but also difficult to govern.

You cannot have discretion without variability.

In the past, when we had asked, “Is the tool working correctly?”, we had received an answer. A payroll system is working correctly if the output conforms to its logic. An agent may produce a flawless output, yet its action might still be unauthorized. A result that appears correct might have been obtained from the wrong sources, or a reasonable recommendation might be based on a control having been omitted. In such cases, the outcome may be valid while the process may be invalid.

In elementary school, teachers tell you to show your work, and that idea is reflected here: the output is not evidence of the process.

The change can be summed up in a single sentence: if AI were a tool, you could control it by examining the outcome. But when AI acts as an executor, the result no longer indicates whether the work was properly governed. Now it is essential to consider the reason for the output; otherwise, the user of agentic AI is at the mercy of statistical fluctuations.

Luck should not be considered a strategy.

Why the unit of governance must move

Most organisations are still treating AI as though it were a tool, and this is fair since AI was a quite specific kind of tool until recently. The controls in place ask responsible-use questions such as: has the model been approved? Is the data being handled properly? Has a human reviewed the output? These questions deal with actual problems.

Nevertheless, they are now focused on the wrong element. They look at whether AI is being used and whether the final answer appears correct. The part that now involves risk, however, is the process trajectory. That is, the work carried out between the instruction and the answer. Previously, the controls dealt with the inputs and outputs. Autonomous AI now demands that the trajectory between them be governed.

Three conclusions can be drawn when the unit of governance is transferred to the process trajectory; the remainder of this series will look at them.

First, authorization turns from a planned setup problem to an ongoing one. It is not enough to authorize a task initially and then rely on the assumption that the work remained within the required scope. Because of the probabilistic nature of agentic execution, that assumption is no longer safe. The agent makes significant decisions at each step, and any one of these could silently cause the task to go outside its authorized boundaries even though the final result seems correct. Therefore, validation of the task’s authorized definition must occur as it is being carried out.

Second, the process of reviewing becomes more difficult in particular and expensive ways. When a reviewer looks at a completed memo, they can only see the final destination, not the route taken. To actually check the work, they would have to reconstruct the entire path, made up of dozens of mostly invisible steps. Checking the process is valuable and, by hand, costly. The tension does not resolve on its own.

Thirdly, where to assign accountability when agentic AI is wrong. If an unvalidated gate requirement is passed, then accountability must be assigned. The responsibility will fall to a human, typically the individual who signed the memo. However, in many cases they could not see what the agent actually did. Attaching a signature to the end of an opaque process does not establish accountability; it creates a place to assign blame. Assigning accountability without visibility does not result in genuine governance; it merely creates a target for blame.

The deeper subject

Artificial intelligence is the trigger because, on a large scale, it exacerbates the divide that organizations have always been aware of: execution can be shifted while accountability stays in place. For a long time, when employing contractors, firms dealt with the problem of controlling work they cannot see directly, the difficulty of holding someone accountable for a process they have not observed, and the need to keep the effort delegated within its own boundaries. Outsourcing is the usual example. The work is transferred. The firm still bears the responsibility.

Agentic AI transforms that old problem by changing the executor. The delegated actor can now make consequential choices, follow different paths through the same task, and produce work the organization may rely on. The actor, however, does not suffer any of the consequences. Management already has vocabulary for parts of that problem: delegation, agency, outsourcing, monitoring, contracting, governance, and accountability. Agentic AI changes the object those vocabularies must govern.

This series extends the idea of management inheritance to the case of agentic AI. It examines how mandates change, how this change becomes apparent, why evidence of how it changes becomes the main point of control, and why the companies that succeed with agentic AI will be those that allow the execution to take place outside the firm without yet releasing their sense of accountability.

The place to start is the gap the stage-gate memo opened. The output was useful. The question was whether it was governed. Those turn out to be two different things. Once AI becomes an executor, that distinction is the whole game.

Next: A Useful Output Is Not a Governed Output.

Part of Governed Execution: Managing Agentic AI — a series on the management discipline required when AI executes work, but firms still answer for it.