From External Orchestration to Intrinsic Continuous Agency

Language-model agents increasingly preserve memory, stream perception, and schedule repeated inference, producing behavior that appears persistent even when the underlying model remains episodic. We use cognitive continuity to denote the stronger capability in which an agent's internal cognitive state itself keeps evolving through time, including without meaningful new external input, and this evolution can change what the agent remembers, infers, intends, and ultimately whether, when, and how it acts. Existing systems realize important parts of this capability through external orchestration and continuous autoregressive processing. We argue that cognitive continuity should also be pursued as an intrinsic learned model capability rather than primarily reconstructed around episodic inference or an advancing interaction history. Our central hypothesis is not one of expressive impossibility: strong harnesses and autoregressive models may reproduce the same surface behavior. Architectural value need not require an expressivity separation: architectures can realize overlapping functions while differing substantially in the inductive biases, coordination pathways, and optimization problems they expose to learning. Rather, the locus of continuity determines which representations, coordination pathways, and state transitions are jointly learned versus externally prescribed. As one concrete instantiation, we outline a Global Workspace Theory-inspired architecture organized around a persistent shared latent state whose recurrent dynamics evolve independently of discrete interaction turns. We conclude with comparative tests and a benchmark hierarchy separating floor awareness, reasons to manifest, and genuinely endogenous continuation.