Patrick Debois describes a shift from developers repairing individual agent outputsAn AI coding agent is a tool-using AI system that can inspect, modify, and validate software within a repository. to teams improving the system that produces them. Reusable context, tests, documentation, guardrails and feedback loopsAn AI feedback loop occurs when AI outputs or their consequences become inputs that influence later model or agent behavior. preserve engineering craft while reducing the number of human corrections each task needs.
Team rituals change with this model. Planning separates well-scoped work that agents can take directlyAgent task scoping defines the objective, boundaries, inputs, permissions, deliverables, and completion evidence for an AI agent assignment. from ambiguous decisions that still require conversation, while retrospectives focus on recurring harness failures that should be fixed once for everyone.
Patrick Debois extends the pattern to an internal platform with maintained skill registriesAn AI agent skill registry is a maintained catalog of reusable, versioned capabilities available to AI agents., evaluation systems, security controls, cost visibility and a small set of supported paths. He recommends measuring declining human touches and increasing reuse rather than relying on token spending or simplistic productivity comparisons.
At the organizational level, leaders must give team leads and platform owners a clear mandate, train people to combine AI leverage with engineering judgment and choose autonomy according to riskRisk-based AI agent autonomy gives agents more independent authority only when the likelihood and impact of failure are acceptable.. The likely destination is a partially autonomous factory whose knowledge and controls improve continuously.
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