Patrick Debois describes a shift from developers repairing individual agent outputs to teams improving the system that produces them. Reusable context, tests, documentation, guardrails and feedback loops 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 directly 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 registries, 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 risk. The likely destination is a partially autonomous factory whose knowledge and controls improve continuously.
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