Stop Building AI Agents. You're Overengineering Everything.

The article argues that most companies claiming to build AI agents are actually overengineering simple problems with excessive LLM abstractions when deterministic workflows would suffice. Drawing from Gerus-lab's experience shipping 14+ products, the author highlights that real agentic behavior requires persistent memory, dynamic tool selection, and self-correction—whereas most production use cases only need good prompt design, structured outputs, and minimal LLM calls. The recommended pattern is using LLMs as classifiers with strict schemas rather than autonomous decision-makers.