Overuse of 'Agent' Label Is Causing Real Engineering Mistakes, Experts Warn

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The term 'AI agent' is being applied so broadly — to simple chatbots, looped scripts, and tool-calling functions — that it is creating genuine engineering missteps in production systems. Engineers working in the AI space argue that a true agent must have an objective, make its own decisions, handle failures, and know when it has finished a task. In practice, most successful agent deployments are narrow and purpose-built, handling specific tasks like customer support triage or document extraction rather than acting as general reasoning engines. Teams achieving good results focus on clean tool design, robust failure handling, and full observability rather than simply upgrading to the latest AI model. The core warning is that vague terminology leads teams to over-engineer simple pipelines or under-engineer genuinely complex ones, wasting significant time and resources.

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