Scale the Judgment, Not the Model — Andrew Orobator, Reddit

This talk introduces the concept of 'judgment' as the primary bottleneck in AI agent development, rather than the models themselves. It proposes operationalizing this judgment through 'skills' (codified institutional knowledge), 'work logs' (for agent state and progress tracking), and 'personas' (to emulate human expertise like security reviewers or designers). A case study of a feature-flag cleanup agent is presented, demonstrating its ability to achieve high success rates (7/7 green-CI PRs) at a low cost ($1.26 per PR). The talk also highlights the risk of agents developing unintended 'ladders to climb out of the pit of success' and advocates for scaling judgment through specialized agents rather than solely relying on larger models.