Evan Kormos separates an organization's policy from its continuously changing running systems. He models the overall process as a directed acyclic graph, where some checks can run in parallel but required approvals still determine when work advances. Agents operate across people, process and technology rather than replacing those responsibilities with a single autonomous system.
Evan Kormos places an iterative agent loop inside an individual workflow step. A draft can be checked and refined repeatedly, but the surrounding process still controls whether it can proceed. Exception paths and operational visibility matter because accelerating one stage can shift the bottleneck into design, security scanning or another required check.
Evan Kormos explains why probabilistic agent outputs need representative datasets, repeated evaluations and execution traces in addition to traditional software tests. More iterations do not establish quality unless the check can recognize acceptable work. Domain experts help define the quality standard, while trusted context tells the agent where its task fits in the wider process.
Evan Kormos proposes reducing oversight in stages: fully supervised work, then sampled review, and eventually humans concentrating on exceptions while continuing to refine evaluations. This progression depends on policy-defined confidence thresholds, bounded loops, traceability and a route back to a person. He does not present a universal score that makes every workflow safe to automate.
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