Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
We are building agents to execute functions — which means we are automating the process on paper, not the one that actually runs.
Someone asked me what I think the biggest mistake people are making with AI. My answer is simple: we are building agents to execute functions.
That sounds like exactly what an agent is for. It is also exactly where it goes wrong.
An agent built to execute a function has already assumed the function is correct. Auto-approve the standard purchase order; route every ticket by the fixed playbook; run the sourcing event exactly as the SOP describes — each one takes the process as given and makes it faster, cheaper, and more autonomous. But if that process reflects how work is supposed to happen rather than how it actually happens — and in most organizations, it does — then the agent doesn’t close the gap between the two. It automates the gap, at machine speed, and calls the result progress.
I have watched this same pattern repeat through every technology wave — ERP, SaaS, cloud, and now agentic AI. The tool changes; the mistake doesn’t. It is the same error I documented at Canada’s Department of National Defence in the late 1990s, where a contract stuck at 51% next-day delivery had nothing to do with procurement and everything to do with an incentive two departments away that no process map contained. Automate the procurement function exactly as it was written, and you would have optimized, beautifully, for the thing that was actually broken.
What is different now is speed and scale. An agent does not pause to notice that the workflow it is executing stopped reflecting reality years ago — it just runs it, faster, everywhere. One Gartner analyst recently named the same trap in nearly identical terms: fed documentation written for the audit rather than for reality, AI faithfully optimizes a workflow the organization abandoned years ago. When the framework-builders describe the trap in the same words, it is no longer one advisor’s opinion — it is the pattern surfacing.
The car — and the lie in the sales pitch
Here is the analogy I keep returning to. A better car does not make someone a better driver. The equipment is not the skill.
And that, unfortunately, is precisely how AI is being sold — as equipment that makes you a better driver. Buy the platform, deploy the agent, and capability is supposed to follow. It doesn’t. A faster car in the hands of someone headed to the wrong destination only arrives at the wrong place sooner.
What an agent should actually be
Agents are more than instructional extensions of human dictates. Reducing them to function-executors wastes what they are genuinely good for.
Used well, AI is a collaborative reasoning partner — tapped in real time for meaningful feedback and for decision transparency, and only then for execution. But read that carefully, because the distinction is the whole game: the partner does not replace the driver. Across more than eighteen hundred hours of single- and multimodel research, my own work keeps landing on one stubborn finding:
AI is a bandwidth and access upgrade, not an intelligence upgrade.
It is tireless and scalable; it is not wiser than the person orchestrating it. The human stays at the wheel — and now has a partner who can reason out loud, surface disagreement, and show its work before anything executes.
That phrase — decision transparency — is not a slogan in my hands. It is the thing I built. RAM 2025™ is a multimodel panel that shows where independent models agree, where they disagree, and why. That is decision transparency, made operational. Most of what is sold as “AI” buries its reasoning inside a confident answer. The point of the architecture is to do the opposite: expose the reasoning so a human can judge it, before the agent acts on it.
The order has not changed
So the sequence is the one it has always been — only the technology in the last step is new. Understand how the work actually happens. Validate that the function is real before you automate it — the discipline I have come to call Invariant Physics™. Keep a human orchestrating the reasoning. Let AI be the partner that makes that reasoning faster and more transparent. Then, and only then, build the agent to execute.
Build an agent to execute a validated reality, with a person at the wheel, and the execution is meaningful. Build one to execute a function no one validated, and you have not deployed intelligence. You have automated the mistake AI was meant to address.
Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
Hansen Models™ | Invariant Physics™ | RAM 2025™ | ARA™ (Augmented Reasoning Architecture™)
-30-
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The Biggest Mistake People Are Making with AI
Posted on July 23, 2026
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Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
We are building agents to execute functions — which means we are automating the process on paper, not the one that actually runs.
Someone asked me what I think the biggest mistake people are making with AI. My answer is simple: we are building agents to execute functions.
That sounds like exactly what an agent is for. It is also exactly where it goes wrong.
An agent built to execute a function has already assumed the function is correct. Auto-approve the standard purchase order; route every ticket by the fixed playbook; run the sourcing event exactly as the SOP describes — each one takes the process as given and makes it faster, cheaper, and more autonomous. But if that process reflects how work is supposed to happen rather than how it actually happens — and in most organizations, it does — then the agent doesn’t close the gap between the two. It automates the gap, at machine speed, and calls the result progress.
I have watched this same pattern repeat through every technology wave — ERP, SaaS, cloud, and now agentic AI. The tool changes; the mistake doesn’t. It is the same error I documented at Canada’s Department of National Defence in the late 1990s, where a contract stuck at 51% next-day delivery had nothing to do with procurement and everything to do with an incentive two departments away that no process map contained. Automate the procurement function exactly as it was written, and you would have optimized, beautifully, for the thing that was actually broken.
What is different now is speed and scale. An agent does not pause to notice that the workflow it is executing stopped reflecting reality years ago — it just runs it, faster, everywhere. One Gartner analyst recently named the same trap in nearly identical terms: fed documentation written for the audit rather than for reality, AI faithfully optimizes a workflow the organization abandoned years ago. When the framework-builders describe the trap in the same words, it is no longer one advisor’s opinion — it is the pattern surfacing.
The car — and the lie in the sales pitch
Here is the analogy I keep returning to. A better car does not make someone a better driver. The equipment is not the skill.
And that, unfortunately, is precisely how AI is being sold — as equipment that makes you a better driver. Buy the platform, deploy the agent, and capability is supposed to follow. It doesn’t. A faster car in the hands of someone headed to the wrong destination only arrives at the wrong place sooner.
What an agent should actually be
Agents are more than instructional extensions of human dictates. Reducing them to function-executors wastes what they are genuinely good for.
Used well, AI is a collaborative reasoning partner — tapped in real time for meaningful feedback and for decision transparency, and only then for execution. But read that carefully, because the distinction is the whole game: the partner does not replace the driver. Across more than eighteen hundred hours of single- and multimodel research, my own work keeps landing on one stubborn finding:
AI is a bandwidth and access upgrade, not an intelligence upgrade.
It is tireless and scalable; it is not wiser than the person orchestrating it. The human stays at the wheel — and now has a partner who can reason out loud, surface disagreement, and show its work before anything executes.
That phrase — decision transparency — is not a slogan in my hands. It is the thing I built. RAM 2025™ is a multimodel panel that shows where independent models agree, where they disagree, and why. That is decision transparency, made operational. Most of what is sold as “AI” buries its reasoning inside a confident answer. The point of the architecture is to do the opposite: expose the reasoning so a human can judge it, before the agent acts on it.
The order has not changed
So the sequence is the one it has always been — only the technology in the last step is new. Understand how the work actually happens. Validate that the function is real before you automate it — the discipline I have come to call Invariant Physics™. Keep a human orchestrating the reasoning. Let AI be the partner that makes that reasoning faster and more transparent. Then, and only then, build the agent to execute.
Build an agent to execute a validated reality, with a person at the wheel, and the execution is meaningful. Build one to execute a function no one validated, and you have not deployed intelligence. You have automated the mistake AI was meant to address.
Truth Is Believing. Accuracy Is Knowing. Outcome Is Proof.™
Hansen Models™ | Invariant Physics™ | RAM 2025™ | ARA™ (Augmented Reasoning Architecture™)
-30-
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