How to Know When an AI Agent Can Work Unsupervised — Evan Kormos, Coinbase

Evan Kormos, a player-coach at Coinbase with 15 years in enterprise financial services, lays out a practical way to bring agents into policy-driven, regulated workflows.

He models processes as DAGs and loops, explains why a loop is only as trustworthy as its check, and shows how evals and traces become a new confidence mechanism. Agents then move through three stages: fully supervised, then sampled, then humans handling only exceptions. Chapters: - 00:00 Policy is the original system prompt - 02:53 Where agents fit: people, process, technology - 03:49 Workflows as DAGs - 05:31 Inside a node: the loop - 07:16 Exception flows and bottlenecks - 08:59 Quality inputs, quality outputs - 10:43 The loop is only as good as the check - 11:47 Evals as a confidence mechanism - 12:39 Which steps should an agent own? - 14:29 Supervised, then sampled, then exceptions only - 15:31 Takeaways Evan Kormos — https://www.coinbase.com Join us in SF for the second annual AIE CODE summit: https://aie.codes our last flagship summit of 2026. Applications live now!

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