The article argues that the core economic feature of agentic AI is discretion. Unlike traditional automation, agents can interpret objectives, choose tools, revise plans and decide when a task is complete. That flexibility creates value because it lets software handle ambiguity and exceptions, but it also makes costs less predictable as reasoning, tool use, retries, verification and human review vary across tasks.

The relevant metric therefore shifts from cost per model call to cost per completed objective. The piece introduces an “autonomy frontier”: additional discretion helps until its value begins to flatten while compute costs, latency and operational risk keep rising. The goal is not maximum autonomy, but the right authority for the task. Enterprises should define tool access, spending limits, approval points, verification and escalation rules, treating agent design as an economics-of-delegation problem before an architecture problem.