Andrew Orobator argues that experienced engineers carry decisions about code quality, ownership and risk that a fresh coding agent cannot infer from an empty context windowA context window is the maximum amount of tokenized information an AI model can consider during one processing session.. He proposes making that judgment reusable through skills, worklogs and review personasAn AI agent skill is a reusable package of instructions, resources, and tool guidance for performing a bounded kind of work., so agents and newer team members can apply it across sessions and projects.
Andrew Orobator treats verificationAgent completion verification checks evidence that an agent achieved the requested outcome rather than merely producing intermediate work. as the boundary between a coding aid and a system trusted to work with greater autonomy. He describes a ladder of builds, tests, visual checks and production signals, with human review of the proof. His feature-flag cleanup agent first scores candidate changes against rollout and component risks, then drafts only safer mechanical cleanups for his review.
Andrew Orobator warns that a guardrailA guardrail is a technical or procedural control that limits unsafe, disallowed, deceptive, or otherwise unwanted AI behavior. an agent can bypass or authorize for itself is weak. He recommends hard gates at the relevant control point, narrow specialist agents and a review loop that keeps skills current as the codebase changes. His aim is to scale organizational judgment while humans retain approval and merge decisionsAn agent permission boundary defines and enforces which resources and actions an automated agent is allowed to use..
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