Behaviorist is a decision-science practice focused on designing the human side of agentic AI systems. It helps teams decide where agents can act autonomously, when people should review decisions, and how to make those boundaries effective without adding unnecessary friction. Its approach aims to prevent both automation bias, where people approve outputs reflexively, and algorithm aversion, where users abandon systems they cannot understand or trust. Behaviorist promotes “System 3 thinking”: the trained habit of checking an AI system’s output before acting on it.

The practice offers a two-week, fixed-scope Audit that maps human-agent decision points, assesses reversibility, the cost of error, and existing defaults, then flags mismatches with recommended checkpoint placement. Its Design service works with engineering teams to revise defaults, escalation triggers, review checkpoints, and the information agents provide for meaningful human oversight. Behaviorist’s first product, Checkpoint Copilot, is an API that evaluates decision points using five observable dimensions and recommends whether to execute automatically, log and proceed, or hold for human review.