The Real Cost of AI Agents

Everyone is talking about autonomous AI agents like they're cheap digital employees, but the real story is far more complicated. In this video, I break down what actually happens when you let AI agents plan, loop, call tools, retry steps, update memory, and collaborate across workflows. On paper, the token costs can look surprisingly low. In practice, those costs multiply fast when you add multiple agents, long reasoning chains, browsing, orchestration, observability, security controls, human review, and enterprise integrations.

I walk through real examples across customer support, software engineering, research, and security operations to show how a few "helpful" agents can quietly turn into a meaningful budget line item. You'll see why the biggest expense often isn't the model itself, but the system wrapped around it.

This is not an anti-AI video. It's a reality check for founders, operators, technologists, and business leaders trying to understand when agentic AI is worth the money—and when simpler automation is the better choice. If you're building with AI in 2026, this is the cost conversation you need to have before scaling. By the end, you'll know how to think about agent cost per outcome, not just per prompt, model call, or demo alone.