Why I'm Betting Against AI Agents in 2025

But let’s say you solve the reliability problems and the economics. You still have to integrate with the real world, and the real world is a mess.

Enterprise systems aren’t clean APIs waiting for AI agents to orchestrate them. They’re legacy systems with quirks, partial failure modes, authentication flows that change without notice, rate limits that vary by time of day, and compliance requirements that don’t fit neatly into prompt templates.

Here’s the uncomfortable truth that every AI agent company is dancing around: error compounding makes autonomous multi-step workflows mathematically impossible at production scale.

The gap between “works in demo” and “works at scale” is enormous, and most of the industry is still figuring this out.

Production systems need 99.9%+ reliability. Even if you magically achieve 99% per-step reliability (which no one has), you still only get 82% success over 20 steps. This isn’t a prompt engineering problem. This isn’t a model capability problem. This is mathematical reality.

After building AI systems, here’s what I’ve learned:

1. Error rates compound exponentially in multi-step workflows. 95% reliability per step = 36% success over 20 steps. Production needs 99.9%+.

2. Context windows create quadratic token costs. Long conversations become prohibitively expensive at scale.

3. The real challenge isn’t AI capabilities, it’s designing tools and feedback systems that agents can actually use effectively.