◇ Could this help me?
Yes, with the marketing discounted. The underlying observation is sound and matches what is actually happening: once an agent runs a workflow end to end, the scarce skill becomes evaluating its output, spotting where it fails, and proving whether it paid off, and that skill lives with operations and customer success people more often than with engineers. Treat the specific predictions as unsupported: the video's claim that this role will be standard at every AI-first company within 18 months is a headline, not a finding, and the free playbook is gated behind commenting rather than published. General practice, and the three named courses are real published resources worth going to directly.
I want to position myself for a role overseeing AI agents rather than building them. Based on my background, help me identify which of my existing skills map onto the parts of that work that matter: defining workflows, evaluating output quality, diagnosing failures, and quantifying impact. Then design a concrete practice project I could do on my own, where I set up an agent for a real workflow, define what good output means, measure where it fails, and write up the result as evidence of the judgment skills. Point me at freely published courses and documentation for the gaps, not paid gated material.