Kent C. Dodds uses product-engineering examples to separate writing software from solving a valuable user problem. Faster AI implementation can amplify weak assumptions and unnecessary complexity, so engineers still need to understand workflows, constraints, architecture and the cost of getting a decision wrong.
The workshop recommends learning from actual user behavior, existing workarounds and recurring problems instead of asking for hypothetical praise. Small prototypes, explicit constraints and feedback loops help teams test their assumptions before investing in a large build. Agents benefit from a well-designed environment, but responsibility for the product and technical tradeoffs remains with the engineer.
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