Stop Building AI Agents: Use Smarter LLM Workflows

Rather than building complex multi-agent systems that become brittle and difficult to debug, most LLM use cases are better served by simpler workflow patterns that keep humans in control of the task flow. The author argues that agent frameworks create the illusion of progress through complexity, but in practice agents fail due to tool selection errors and task management breakdowns—real problems he encountered building a three-agent research system—and that a deliberate progression from basic LLM augmentation through retrieval, tool use, and finally agentic control (only when necessary) prevents unnecessary failure modes.

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