Agentic Mechanics: From Resolution to Autonomous Execution. How Reuse, Defaults and Risk Minimisation Reshape Decision-Making

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This working paper examines what happens when AI-mediated discovery moves beyond resolving questions and begins to execute decisions. It introduces Agentic Mechanics — the behavioural dynamics through which autonomous systems move from resolution to reuse, from reuse to defaults, and from defaults to increasingly persistent operational pathways. The paper explores why reconsideration can become a cost rather than a virtue, why repeated successful execution can make reuse more valuable than renewed search, and why defaults can function as mechanisms for reducing uncertainty and operational risk. It examines how agentic systems can compress markets by reducing the frequency of comparison, why visibility may become a lagging indicator of selection, and why conventional attribution becomes increasingly difficult once decisions are embedded in reusable pathways. The paper also develops the concepts of default literacy, default governance and independent observation, addressing a central paradox of autonomy: the more effectively a system stops reconsidering established decisions, the more important it becomes to have an independent mechanism capable of detecting when those decisions should be reconsidered. Agentic Mechanics forms part of the broader Elsewhere Systems Framework, an open theoretical framework for understanding how AI systems discover, interpret, trust, resolve, select and increasingly execute decisions. This document synthesises the twelve-part Agentic Mechanics series into a single reference paper and proposes observable hypotheses for further research and testing.