The idea behind autonomous AI sounds quite attractive and simple. Give a powerful model a goal, access to the right systems, and enough compute, and just let it decide how to get the job done on its own.

That might sell well during a product demonstration. But it’s a risky and expensive way to run an enterprise.

Business leaders I speak with are wrestling with a different reality: how much authority they can safely hand over to AI. They often ask agents to repeatedly reason their way through work from scratch when trusted processes already exist. Meanwhile, there’s rarely a day that goes by without more headlines about agents doing unintended things.

AI can be extraordinarily powerful in helping companies design better ways of working. Once the process is understood, the priority should be executing it reliably and applying AI reasoning where it actually adds value.

More code, more problems

I’m hearing two misconceptions about AI with alarming regularity.

The first is that AI can code for whatever the business needs. Sure, AI coding tools can generate extraordinary amounts of software at extraordinary speed. We certainly use them ourselves in the right situations. But that speed introduces a different problem: companies can now generate software faster than they can understand, govern, and maintain it.

Every enterprise has workflows that determine how it operates. A bank has established procedures for handling a disputed transaction. An insurer has rules for processing a claim. These processes represent years of institutional knowledge, regulatory interpretation, and best practices.

Sure, AI can generate code for this in seconds. But if the business logic becomes buried inside volumes of code, the company may struggle to understand how the system works or be unable to change it as situations change. Today’s productivity gains become tomorrow’s legacy debt.

The cost of over-reasoning

The second misconception is that autonomous agents should reason through everything at runtime. Large language models inherently produce variable results. Ask the same question twice, and you might get two different answers. That variability is valuable when you are exploring a problem. It becomes a dangerous liability for regulated industries when organizationsprocess a claim, resolve a payment dispute, or make a decision they can’t afford to get wrong.

There are certainly points in a workflow where AI reasoning brings tremendous value. Specialized agents can be confined to extracting information from documents, summarizing a complicated case, or evaluating an exception. But most of those steps are well established and don’t need to be rethought every time a process runs.

Vibe coding buries the process logic. Autonomous agents continually reinvent it. Both approaches give AI far more responsibility than it needs. After decades spent making enterprise systems more understandable and adaptable, businesses risk adding complexity at machine speed.

Put AI reasoning where it belongs

A more disciplined approach starts with deciding when we want AI to think.

For example, generative reasoning is extraordinarily valuable at design time. Use it to rethink processes, explore possibilities, identify edge cases, and challenge how work should be done. This is where creativity is an asset, not a liability.

Once the process has been designed and approved, it becomes the blueprint for running the business. At runtime, execute itpredictably while bringing in specialized agents in the moments when additional judgment adds value.

Consider how a restaurant operates. The test kitchen is where the chef experiments with ingredients and techniques until he gets the recipes just right. Once dinner service begins, the line cooks don’t reinvent those recipes with every order. They work from approved recipes, making adjustments when needed. That’s how a restaurant delivers consistent quality at scale.

Enterprise AI needs the same discipline. An AI harness puts structure around the raw power of AI models. Use AI reasoning to determine the best way agents should work, then govern their execution with workflows, rules, data, and controls to ensure they do it right.

Control the process, control everything around it

This approach also makes costs easier to predict. Excessive reasoning consumes excessive compute. Paying for a model to repeatedly rework a problem it has already solved adds expense without necessarily improving the result – and may even make it worse.

A harness also gives enterprises flexibility as the models evolve. Capabilities will improve, prices will change, and specialized models will emerge. But not every task requires the most advanced models. Companies can choose the right model for each job while applying the underlying workflows that control how they work.

I have spent more than 40 years in enterprise technology and watched wave after wave of disruption reshape how businesses operate. Generative AI may be the most exciting yet. But every major shift has reinforced the same lesson:embrace what’s new while preserving the principles that keep systems understandable, adaptable, and under control.

As AI gets smarter, that discipline becomes even more important. The companies that get the most from AI will know where reasoning creates value and where predictable execution still matters.

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