Non-Agentic AI Architecture

• Non-agentic AI architecture is a design paradigm where systems operate via fixed algorithms without simulating goal-driven agency.
• It leverages mechanisms such as tensor-based computation, generative models, and network dynamics to realize emergent, system-level behavior.
• Practical applications span swarm robotics, soft-matter computing, and biohybrid constructs, highlighting robust performance through material intelligence.

Non-agentic AI architectures designate systems or processes that execute programmed tasks without embodying or simulating the impression of agency; they operate strictly via direct instruction or fixed algorithms, absent constructs such as “goals” or “beliefs.” This paradigm stands in contrast to both agentic systems—those designed to evoke semi-autonomous, goal-directed behavior—and agential systems—currently only attributed to biological organisms, characterized by full self-production and self-maintenance (autopoiesis). The non-agentic perspective advocates for architectures and formalisms that emphasize emergent properties, system-level dynamics, and substrate-driven intelligence over anthropocentric abstractions and discrete agent boundaries (Gardner et al., 13 Sep 2025).

1. Definitions, Taxonomy, and Conceptual Boundaries

Non-agentic systems are defined explicitly as “tools or processes programmed to perform tasks without giving the impression of agency. They operate based on direct instruction or fixed algorithms” and do not invoke explanatory frameworks of goals or beliefs. The taxonomy distinguishes:

System Type	Defining Properties	Exemplars
Agentic	Impression of autonomy; semi-autonomous, goal-directed	LLM-based agents
Agential	Fully autonomous, self-producing, autopoietic	Biological organisms
Non-agentic	Algorithmic tools; no impression of goals or beliefs	Materials, micromotors

Agentic systems present engineered scaffolding for autonomy but lack deep functional autonomy. Agential systems comprise living systems with intrinsic, life-like self-organization. Non-agentic systems deliberately eschew anthropomorphizing constructs, focusing instead on mechanistic operation and physical process (Gardner et al., 13 Sep 2025).

2. Mathematical and Formal Abstractions

Non-agentic as well as agentic AI systems are fundamentally grounded in high-dimensional tensor operations and generative modeling frameworks. Constructs such as beliefs and goals are described as “glosses on these tensor operations.” The principal mathematical themes include:

• Tensor-based computation: Underlies training, inference, and system updates; all “beliefs” and “intentions” are reducible to transformations on tensors.
• Generative models: The Active Inference (AIF) framework and Free Energy Principle formalize inference and adaptive behavior via the minimization of variational free energy, summarized as

$F \approx \mathrm{KL}[q(s)\|p(s|o)] - \log p(o)$

where $q(s)$ is the approximate posterior, $p(s|o)$ is the likelihood, and $p(o)$ is the probability of observations.

• Markov blanket formalism: Factorization of state variables into internal and external sets to formalize system-environment boundaries, without requiring explicit agent demarcations.
• Control as Inference and RL: The mathematical equivalence between certain formulations of AIF and reinforcement learning (RL) is noted, with RL directly learning policies $\pi(a|s)$ for action selection, in contrast to encoding beliefs as priors. This highlights the minimal necessity of “agents” in well-posed control and inference tasks.
• System-level dynamics: Non-agentic architectures often invoke dynamical systems theory and network interactions, dispensing with discrete agent boundaries in favor of continuous sensorimotor couplings (Gardner et al., 13 Sep 2025).

3. Exemplary Systems and Case Studies

Non-agentic concepts are operationalized in several domains, emphasizing collective behavior, emergent intelligence, and material computation:

• Micromotors and soft robots: Systems with simple, local propulsion or activation rules self-organize into dynamic, adaptive patterns, as exemplified by physically embodied micromotors (Hu et al. 2018; Ceylan et al. 2017).
• Collective robotics/swarm systems: Ant-inspired robotic swarms (Rubenstein et al. 2014) and schooling behaviors in fish-mimetic simulations (Couzin 2009; Múgica et al. 2022; Puy et al. 2024) demonstrate group-level behaviors unattached to individualized agentic purpose.
• Xenobots and anthrobots: Multicellular constructs derived from frog cells (Kriegman et al. 2020; Gumuskaya et al. 2024; Levin 2024; Solé et al. 2024) manifest distributed locomotion, self-repair, and adaptation from collectively emergent dynamics.
• Soft-matter and metamaterials: Adaptive materials exhibit intrinsic sensing, memory, and control without hierarchical control architectures (Baulin et al. 2025a; Kowerdziej et al. 2022).
• Biological exemplars: Plant tissue mechanics (e.g., adaptive stomatal pore geometry; Durney et al. 2023) leverage system-level physical properties instead of goal-driven computation (Gardner et al., 13 Sep 2025).

4. Principles of Material Intelligence and Unconventional Computing

The “matter computes” hypothesis asserts that intelligence may fundamentally emerge from substrate-specific dynamics, challenging the primacy of software-hardware separation:

• Reservoir computing in physical media: Utilizing the transient physical dynamics of materials as computational reservoirs obviates the need for explicit agent models (Lee 2022; Solé & Seoane 2022).
• Distributed adaptation: Soft-matter and metamaterials can perform distributed sensing, learning, and adaptation via their physical connectivity rather than top-down symbolic control.
• Intrinsic autonomy: Synthetic tissues and biohybrid constructs (Davies & Levin 2023) exemplify bottom-up, agential material intelligence, although true agentiality is currently limited to living systems.
• Neuromorphic platforms: Device physics in brain-like, spiking neural hardware further dissolves the division between computation and material substrate (Kozachkov et al. 2023).
• Systemic computation: The core orientation is toward system-level properties—information flow, energy dissipation, dynamical couplings—eschewing anthropomorphic frameworks (Gardner et al., 13 Sep 2025).

5. Comparative Analysis: Agentic, Agential, and Non-Agentic Paradigms

A critical comparison delineates foundational design and evaluation differences:

Paradigm	Design Emphasis	Benefits	Limitations
Agentic	Symbolic scaffolding; modularity	Familiar conceptual tools	Risk of misleading anthropomorphism
Agential	Self-production; autopoiesis	Intrinsic autonomy (only biology)	Not yet technologically reproducible
Non-agentic	System-level dynamics; substrate	Mechanistic clarity; robustness	Challenges in explainability, human intuitions

Agentic systems encode high-level constructs (beliefs, intentions) into discrete modules, often for heuristic or explanatory convenience. Agential systems aspire to the self-organizing, self-sustaining characteristics of living systems but are currently only realizable in biology. Non-agentic systems exploit intrinsic material or system dynamics, gaining robustness and clarity at the expense of intuitive, human-like interpretability. The central shift is from designing bounded, metaphor-laden agents to engineering sensorimotor loops and emergent patterns in heterogeneous substrates (Gardner et al., 13 Sep 2025).

6. Research Trajectories and Design Considerations

Key recommendations and open avenues for non-agentic architectures are enumerated as follows:

• Atlas of Opportunity: Systematic pairing of methods (e.g., RL robust to Goodhart’s Law, formal agent verification with modal logic) and application domains (e.g., algorithmic mimicry in robotics, LLM-based social simulation) with explicit critiques or validation challenges.
• Emergence from continuous interaction: Prioritize research into representations and competencies arising via embodied, sensorimotor engagement rather than assuming preset goals or planning modules.
• System-first engineering: Orient design around the induction of desirable dynamic patterns in material substrates (soft matter, metamaterials) rather than assembling agents with assigned functions.
• Sustainability and ethical frameworks: Integrate principles such as Goodhart’s Law, Jevons Paradox, Chesterton’s Fence, and Ashby’s Law into the design, governance, and operational monitoring of intelligent systems, emphasizing properties at the system rather than agent level.
• Operational metrics: Substitute “agency talk” with quantitative system metrics—information flow, energy consumption, dynamic coupling strength—for clarity and reproducibility.
• Transdisciplinary synthesis: Advance non-agentic intelligence by blending theories and practices from complex systems, biology, unconventional computation, and materials science, aiming for scalable and ethically robust architectures.

A plausible implication is that further progress in artificial general intelligence may hinge on transcending both agentic metaphors and classical digital paradigms, moving fully towards architectures that leverage emergent, substrate-specific, distributed properties and system-level organization (Gardner et al., 13 Sep 2025).