Intelligent Insights
Reasoning Is Expensive. Execution Should Be Cheap
AI agents can plan, re-plan, call tools, reflect, and keep reasoning. That flexibility is powerful—but every reasoning step costs money. In Part 2 of Enterprise AI in Production , I look at one of the biggest challenges in taking agentic AI from demo to production: unpredictable cost . The important question isn't simply how much an AI model costs. It's: What does one business decision cost—and can we bound that cost? When an agent repeatedly reasons through a problem it has already solved hundreds or thousands of times, we're paying premium token costs to rediscover something the system already knows. A better pattern is to let reasoning earn its retirement . Use AI for genuinely new, ambiguous, or difficult problems. Once a behavior becomes stable and repeatable, convert it into a deterministic rule, workflow, cached decision, or ordinary code. You don't lose the intelligence. You bank it. In this episode: • Why autonomous agents can create unpredictable operating costs • Why cost per decision matters more than token cost alone • The hidden cost of repeatedly solving the same problem • How to identify workflows that should move off the reasoning path • Why reasoning should be a phase—not a permanent state • How hybrid AI architectures can improve enterprise ROI The goal isn't to use less AI. It's to make sure we're paying for reasoning only when reasoning is creating new value.
