As AI agents become reliable for complex, multi-step tasks, the critical human role will shift from execution to verification. New jobs will emerge focused on overseeing agent processes, analyzing their chain-of-thought, and validating their outputs for accuracy and quality.

To find talent capable of managing an AI stack, traditional interviews are insufficient. A better test is to provide candidates with platform credits (e.g., Replit) and challenge them to build a functional agent that automates a real business task, proving their practical skills.

Your mental model for AI must evolve from "chatbot" to "agent manager." Systematically test specialized agents against base LLMs on standardized tasks to learn what can be reliably delegated versus what requires oversight. This is a critical skill for managing future workflows.

To accurately assess candidates, interviews must be split. One part must be a "Zero AI" test to evaluate raw problem-solving ability and foundational knowledge, complete with cheat detection. The other part must be an "AI-Max" test to assess their skill in leveraging AI tools to be a "roboticist."

Rather than creating assessments that prohibit AI use, hiring managers should embrace it. A candidate's ability to leverage tools like ChatGPT to complete a project is a more accurate predictor of their future impact than their ability to perform tasks without them.

To evaluate candidates, run the same case study through an AI agent like Claude. This creates an objective performance floor; if a human candidate cannot outperform the AI's output, they fail to meet the minimum standard for the role, providing a practical filter in the hiring process.

To assess a product manager's AI skills, integrate AI into your standard hiring process rather than just asking theoretical questions. Expect candidates to use AI tools in take-home case studies and analytical interviews to test for practical application and raise the quality bar.

To determine the boundary between human and AI tasks, ask: "Would I feel comfortable telling my CEO or a customer that an AI made this decision?" If the answer is no, the task involves too much context, consequence, or trust to be fully delegated and should remain under human control.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.