Start with customer jobs: Build AI products around valuable, repeatable tasks rather than starting with a specific model or AI feature.

Design controlled autonomy: Give agents clear permissions, tools, policies, verification steps, and human oversight based on task risk.

Measure outcomes: Evaluate AI through task completion, accuracy, reliability, cost, safety, human overrides, and measurable customer or business value.

Artificial intelligence has changed the product question. A strong AI product no longer needs to stop at chat, search, summaries, or content generation. The bigger opportunity lies in software that can handle a complete customer task, make decisions within clear limits, use business tools, check its own work, and deliver a useful result. 

For chief product officers (CPOs), this shift changes the product framework. The focus moves from AI features to customer jobs, workflows, agent control, context, tools, evaluation, trust, and business outcomes. The goal is not simply to add a model to an existing product. The goal is to build a product that can complete valuable work.

A strong agentic product starts with a clear customer job rather than a model. Questions about GPT, Claude, or another model should come after the product team defines the problem.

A useful job has a clear outcome, a repeatable process, and measurable value. A support agent may need to resolve a customer issue. A sales agent may need to qualify a lead and schedule a meeting. A finance agent may need to check an invoice and approve a valid payment. Each job has several steps, rules, data sources, and points where a person may need control.

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