Adaptive Resilience

Abstract

Predictive optimization has reached structural limits as the design criterion for adaptive AI systems in high-consequence environments. Systems trained to maximize accuracy against expected future distributions fail under distribution shift, and the failure is not graceful: prediction-optimized systems collapse where the prediction was wrong. This paper argues that the next generation of adaptive AI orchestration must be designed for viability rather than for prediction accuracy — engineered to remain operational across futures that include the unforeseen, the rare, and the actively adversarial. The argument synthesizes three established lineages: Beer's Viable System Model [1], Hollnagel's resilience engineering [2], and Taleb's antifragility [3]. The contribution of this paper is not to restate those frameworks but to articulate viability as a concrete adaptive-AI orchestration discipline: an architecture in which audit-first command flow, modular subsystem construction with strict separation of concerns, bounded recovery pathways, tail-event preparation, and preserved operator authority together produce a system that does not require accurate prediction to remain useful. The discipline is illustrated using MILO, a patent-pending adaptive AI orchestrator [4] submitted to the U.S. Department of Energy under the Genesis Mission [5]. The unifying principle is stated plainly: MILO does not predict the future. It remains viable in any future.

Summary

Plain Language Summary. Most AI systems today are designed to make accurate predictions about the future based on past data — and they fail, often catastrophically, when the future stops resembling the past. In critical-infrastructure environments (power grids, manufacturing lines, nuclear facilities, autonomous robotics, satellite operations), an AI system whose usefulness depends on accurate prediction is a brittle system. This paper argues that the next generation of AI orchestration for high-consequence environments must be designed for viability — the capacity to remain operational, auditable, and human-controllable under conditions the system was not trained to expect — rather than for prediction accuracy. The principle synthesizes Beer's cybernetic viability, Hollnagel's resilience engineering, and Taleb's antifragility into a concrete engineering discipline.

Key takeaways

• Design adaptive systems for viability under non-stationary operating conditions.
• Treat resilience as the ability to maintain function through change, not simply bounce back.
• Anchor adaptive AI governance in operational technology and human-AI risk frameworks.

Concept map

Sources to follow

Use these official references as starting points for the standards context in the full paper.