Explore evidence-led notes on where AI should run, what agents need after the demonstration, and how technical mechanisms change workflow economics. Claims are bounded to their workload and evidence.
A synthetic probability model exposes why one-step accuracy cannot establish reliable long-workflow completion. The practical method replaces that toy arithmetic with repeated end-to-end tests covering state, permissions, tools, checkpoints, severe failures, intervention, latency, cost, recovery, and the exact authority an AI workflow may receive.
