Reasoning Is Expensive. Execution Should Be Cheap

<p>AI agents can plan, re-plan, call tools, reflect, and keep reasoning. That flexibility is powerful—but every reasoning step costs money.</p><p>In Part 2 of <em>Enterprise AI in Production</em>, I look at one of the biggest challenges in taking agentic AI from demo to production: <strong>unpredictable cost</strong>.</p><p>The important question isn&#39;t simply how much an AI model costs.</p><p>It&#39;s:</p><p><strong>What does one business decision cost—and can we bound that cost?</strong></p><p>When an agent repeatedly reasons through a problem it has already solved hundreds or thousands of times, we&#39;re paying premium token costs to rediscover something the system already knows.</p><p>A better pattern is to let reasoning <strong>earn its retirement</strong>.</p><p>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.</p><p>You don&#39;t lose the intelligence. You bank it.</p><p>In this episode:</p><p>• Why autonomous agents can create unpredictable operating costs<br>• Why <strong>cost per decision</strong> matters more than token cost alone<br>• The hidden cost of repeatedly solving the same problem<br>• How to identify workflows that should move off the reasoning path<br>• Why reasoning should be a phase—not a permanent state<br>• How hybrid AI architectures can improve enterprise ROI</p><p>The goal isn&#39;t to use less AI.</p><p>It&#39;s to make sure we&#39;re paying for reasoning only when reasoning is creating new value.</p>