After a year of building AI agents, the author argues that businesses don't actually want autonomous agents—they want specific outcomes like reduced support tickets and manual work, and simpler, reliable solutions often deliver more value than highly autonomous systems.
A year ago I was completely convinced that agents were the future. The vision seemed obvious. Businesses were drowning in repetitive work, language models were getting smarter every month, and autonomous systems appeared to be the logical next step. It felt inevitable that companies would want agents handling research, operations, support, analysis, and countless other workflows. So I started building them. Some of the demos were genuinely impressive. Multi-step planning, tool usage, memory, reasoning, workflow orchestration. Watching these systems operate felt like looking at a glimpse of the future. The problem was that the more conversations I had with actual businesses, the more I noticed a disconnect. The thing that excited builders wasn't always the thing that excited customers. Customers rarely asked for autonomy. They rarely asked for reasoning. They rarely asked for agents. What they wanted was for a specific painful process to stop wasting their time. That's it. The more I listened, the more I realized that businesses don't wake up wanting AI agents. They wake up wanting fewer support tickets, faster onboarding, fewer manual reviews, better reporting, and less repetitive work. An agent is only interesting if it creates one of those outcomes. In many cases, the highest-value solutions turned out to be surprisingly simple. Instead of replacing an entire workflow, they automated one frustrating step inside it. Instead of building a digital employee, they removed a bottleneck. Instead of maximizing autonomy, they maximized reliability. Ironically, those simpler systems often generated more value than the highly autonomous ones. That's what changed my perspective. I still think agents are important. I still think they're going to become a massive category. But I no longer think autonomy itself is the product. The product is the outcome. The product is the time saved. The product is the cost reduced. The product is the problem that disappears. Everything else is implementation detail. I'm curious whether others building agent systems have noticed the same pattern or if your experience has been completely different.
The author argues that in enterprise AI agent development, operational reliability and stability are more critical than high autonomy, advocating for controlled intelligence over fully autonomous systems.
The author argues that most founders requesting AI agents actually need straightforward automations with minimal LLM integration, citing production failures, compliance hurdles, and higher ROI from simpler workflows. The piece provides a practical decision framework to help builders and founders prioritize reliable automations over complex, unpredictable agents.
The author argues that autonomous AI agents are overrated without structured business context and scoped jobs, sharing practical insights from client work where agents run on fixed cadences with human oversight on writes.
The article questions why most autonomous agents are developed for business use rather than for individual users, pointing out a gap in AI accessibility.
The article argues that most businesses need AI agents for automating repetitive workflows rather than just chatbots, and provides a framework for implementation to achieve higher ROI.