As agents take on more of the coding, more of our work involves deciding what they should do and judging the work they return. We may be following several tasks at once, without having written the code ourselves.

It's tempting to hand a task to an agent and mentally cross it off the list, assuming it'll do it as well as we would and on time. With other agents pulling us away to answer questions and check results, we're less likely to challenge that assumption during review. The tests pass, the explanation sounds right, and we click Approve. That's LGTM on AI steroids.

Baruch uses Nobel Laureate Prof. Daniel Kahneman's work on automatic and deliberate thinking, alongside research on attention, fatigue and learning, to examine what working with agents does to our thinking. Experience can make a quick judgment useful, but confidence can also come from an answer that merely sounds familiar.

You'll learn how to decide where deliberate thought is worth the effort, how to build the expertise to judge and debug code an agent writes, and how to arrange work around the limits of human attention. We'll examine what personal habits, team practices and tooling can do to reduce needless effort, improve feedback and help us recover when we're tired.

This talk helps developers and engineering leaders decide which agent results need closer examination, where delegation can cost them practice needed to build expertise, and how to organize work so they have the attention to make those decisions. It brings cognitive research into practical choices about learning, team coordination and feedback from development tools.