Treat AI Agents Like Interns, Not Tools, for Better Engineering Output

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A perspective piece on DEV Community argues that developers get poor results from AI agents because they treat them as disposable code generators rather than contextual collaborators. The author recommends an 'onboarding' approach, which includes providing AI agents with style guides, architectural records, and repository norms to establish institutional context. Teams are also advised to persist successful workflows and prompt templates in version control so agents do not lose knowledge between sessions. Rather than simply rejecting flawed AI output, the author suggests treating errors as learning opportunities by offering direct feedback and requesting revised approaches. The piece concludes that engineering leadership now extends to ensuring both human and AI resources have the context they need to perform effectively.

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