The Digital Press

Many teams rush to build AI agents that control workflows, but these systems often fail due to complexity and brittleness. Instead, simpler structured workflows usually work better, with agents reserved for cases needing human oversight.

Why it matters: Overusing agents leads to fragile systems that break and are hard to debug, wasting time and resources.

The big picture: Simpler LLM workflow patterns—like chaining, routing, and orchestrator-worker—solve most automation tasks more reliably.

The stakes: Using agents for stable enterprise or high-stakes tasks risks errors and unpredictable behavior, unlike deterministic workflows.

Commenters say: Readers appreciate the critique of agent hype, emphasizing cautious use with human oversight, but some argue agents will improve as models evolve.