A KDnuggets piece syndicated through Planet AI argues that agentic AI projects often struggle because teams misunderstand what agents should do and how they should be deployed.
The useful framing is practical: failures are not always evidence that agent technology is broken. They often come from unclear workflows, too much autonomy too soon, or weak evaluation.
For companies experimenting with agents, the lesson is to design bounded tasks, measure outcomes, and add human oversight before expecting fully autonomous systems to perform reliably.