Non-Agentic Systems: Dynamics & Emergence

• Non-agentic systems are defined as mechanisms that lack intrinsic agency and intentionality, operating via fixed or adaptive algorithms without self-directed goals.
• They demonstrate dynamic computational adaptability by integrating environmental interactions, exemplified by models like the Turing machine diagonalization argument.
• These systems underpin complex network dynamics in the technosphere and natural growth processes, challenging traditional isolated system paradigms.

Non-agentic systems are characterized by the absence of intrinsic agency, intentionality, or goal-directed autonomy. Instead, these systems operate according to algorithmic, physical, or adaptive principles that do not presuppose an internal drive, self, or the anthropomorphic capacities typically invoked within agentic frameworks. Non-agentic systems range from algorithmic computational platforms to planetary-scale technological networks and natural growth processes that interact with their environment and evolve in response to external constraints.

1. Formal Distinctions: Agentic vs. Non-Agentic Systems

Non-agentic systems are rigorously distinguished from agentic and agential systems in recent literature (Gardner et al., 13 Sep 2025). Agentic systems are designed to demonstrate internal agency—manifesting semi-autonomy, goal-directed actions, and behaviors modeled on human capacities (e.g., contemporary LLM-based agents). Agential systems, by contrast, are self-producing and fully autonomous, a class currently limited to biological entities.

Non-agentic systems do not embody any self-directedness. Their functionality is defined by direct instruction, fixed or optimized algorithms, and mechanistic execution. The apparent “intelligence” of such systems is an emergent consequence of high-dimensional computation (e.g., tensor operations) or intricate environmental coupling, not intentional planning or motivational states. This distinction is essential when analyzing modern AI systems, tool-like platforms, and different classes of physical or technological networks.

2. Computational Foundations and Dynamic Descriptions

Traditional Turing machines exemplify the static, isolated paradigm: a Turing machine's finite description (its “program”) is fixed and entirely determines future behavior. Once specified, neither the rules nor the operational context are modifiable by environmental influence (0907.4100). Non-agentic systems—particularly those described as interactive or creative—can alter their finite descriptions dynamically through continued interaction with their environment. The capacity to incorporate new information enables the computation of functions outside any pre-specified enumeration.

This is rigorously formalized in the diagonalization argument, where for a given list of computable functions $f_1, f_2, \dots$, a system interacting with its environment can construct the function:

$g(n) = f_n(n) + 1$

This function, while computable, cannot be produced by any fixed Turing machine within that list. The adaptability of non-agentic systems thus extends computational power beyond the Church–Turing thesis when isolation is not assumed.

3. System-Level Dynamics: Technosphere, Networks, and Coevolution

Planetary-scale technological systems (the “technosphere”) are a prototypical example of non-agentic systems with emergent quasi-autonomous dynamics (Donges et al., 2017). Haff's conception describes the technosphere as governed by physical laws (e.g., energy transformation, entropy production) largely independent of human intention. Once established, technological infrastructures may evolve according to deterministic or emergent feedback loops that resist direct human steering due to path dependencies and lock-ins (e.g., fossil fuel investment).

Recent analyses advocate for modeling the Earth system—including its technosphere—through complex adaptive networks. Here, the state of each component (node) and its relationships (edges) evolve according to context-sensitive rules and possibly stochastic environmental interactions. Mathematical representations include node variables $x_i$ and adaptive adjacency matrices $A_{ij}$ encoding the coevolution of social, technological, and biophysical spheres. The recognition of macro-agency suggests that collective actions and institutional changes may exert some control over non-agentic system dynamics, but only as the result of distributed, emergent processes rather than intentional, individual agency.

4. Principles of Growth, Coupling, and Emergence in Natural Systems

Non-agentic natural systems typically develop from a localized “germ” or “seed”—an initial center of organization that recruits energy from its environment (Henshaw, 2022). Growth is described by nonlinear models, commonly logistic-type equations:

$\frac{dN}{dt} = r N \left(1 - \frac{N}{K}\right)$

with $N$ denoting system size, $r$ the intrinsic growth rate, and $K$ the environmental carrying capacity. Three canonical growth trajectories are described:

• Type 1 (“Growth to Exhaustion”): rapid utilization leading to collapse,
• Type 2 (“Growth to Disruption”): unchecked growth destabilizing internal organization,
• Type 3 (successful adaptation): transition to sustainable equilibrium via maturation.

These systems persist via continual cycles of sensing, responding, and adapting to environmental signals—a form of steering which does not presuppose conscious intention but may be modeled as feedback loops or homeostatic regulation. Emergence is the process by which concentrated centers of organization develop and mature, typically captured in integral or segmented growth diagrams.

5. Philosophical Perspective: Open vs. Closed Systems

Philosophy and physics have traditionally prioritized closed systems—isolated entities whose dynamics can be completely described without reference to an external environment (Cuffaro et al., 2021). The open systems view, however, contends that interaction with the environment is fundamental. Rather than treating external influence as a perturbation on an otherwise isolated pair, open systems are represented by dynamical equations that explicitly incorporate environmental interactions as core features.

This shift has implications for ontic, epistemic, and explanatory fundamentality in science and metaphysics: open and non-agentic systems are not merely tools or models but reflect essential principles of system evolution, adaptation, and coevolution within broader networks.

6. Consequences for Theory, Practice, and Sustainability

Non-agentic systems present important challenges and opportunities for formal theory and practical management:

• In computational theory, their ability to continually modify descriptions and expand analytical spaces renders any static formal system incomplete—there will always be functions or behaviors not capturable by a fixed program (0907.4100).
• In technospheric dynamics, non-agentic evolution produces lock-ins that constrain intervention, suggesting that sustainability must be managed via distributed macro-agency rather than through individual or centralized control (Donges et al., 2017).
• For AI, reliance on non-agentic frameworks enables robust, scalable architectures free of anthropocentric bias and ill-defined goal-seeking overlays. System-first approaches grounded in tensor computation, world modeling, and material intelligence are highlighted as future directions (Gardner et al., 13 Sep 2025).
• In natural and cultural evolution, successful systems respect their internal limits and adaptively integrate with their external environments, providing a template for transformation and sustainable development (Henshaw, 2022).

7. Future Directions and Fundamental Research Implications

Non-agentic systems invite deeper investigation into architectures and theories emphasizing self-organization, system-level dynamics, and emergent adaptive behavior. Promising avenues include:

• Development of non-anthropomorphic evaluation frameworks for intelligent behavior,
• Exploration of material-specific computing substrates (e.g., neuromorphic hardware, soft-matter computing),
• Analysis of coevolutionary dynamics within complex networks,
• Formalization of open systems principles in physics and interdisciplinary science.

A plausible implication is that progress in general intelligence, sustainable development, and system design will depend on abandoning rigid agent-centric metaphors in favor of a fundamentally systemic, adaptable, and environment-coupled perspective.