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REVIEW 4 major objections 5 minor 145 references

Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read The paper argues that the 'agent' framing of AI, especially for LLM-based systems, is a sophisticated but limiting facade that obscures the underlying tensor computations, and it proposes shifting research toward system-level dynamics…

desk verdict Coherent but largely derivative critique of the agent paradigm; the quantitative 'diagnosis' is too under-specified to support the paper's strong claims. read the letter →

arxiv 2509.10875 v1 pith:WKL6IU4Q submitted 2025-09-13 cs.AI cond-mat.soft

Jesse Gardner , Vladimir A. Baulin This is my paper · ORCID
classification cs.AIcond-mat.soft
keywords agenticAIagentparadigmanthropocentrismlargelanguagemodelsworldmaterialintelligencesystem-levelknowledgegraphanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the field's near-universal habit of describing AI systems as 'agents'—autonomous entities with beliefs, goals, and intentions—is a limiting framework rather than a neutral description. The authors distinguish agentic systems (semi-autonomous AI, including LLM-based tools, that give the impression of agency), agential systems (fully autonomous, self-producing systems, currently only biological), and non-agentic systems (tools without any agency-like framing). They claim that the 'agentic' view of LLMs, while heuristically convenient, is a sophisticated facade that obscures the real computational mechanisms, which are high-dimensional tensor operations and pattern completion rather than genuine goal-directedness. A quantitative knowledge-graph analysis of the literature is presented as evidence of a persistent gap between theoretical and critical discourse on one side and practical implementation on the other, with critiques of anthropocentrism now more central than the agent concept itself. If correct, this reorientation would push AI research toward world models, system-level and material intelligence, and away from engineering human-like autonomous entities.

What carries the argument

The argument rests on two coupled devices. The first is a tripartite taxonomy: agentic (AI that gives the impression of autonomous, goal-directed behavior without deep autonomy), agential (fully autonomous, self-producing systems, currently only biological), and non-agentic (tools without any agency-like impression). The second is a quantitative knowledge graph built from the paper's literature review: 98 concepts in six categories (Theoretical Concept, Architecture/Model, Entity/System, Method/Technique, Application/Domain, and Critique/Challenge), with edges recording explicit links in the sources. Node influence is measured by a centrality score, interdisciplinarity by the diversity of a concept's connections across categories, and under-explored links by a co-occurrence 'evidence score' heatmap the paper calls the Atlas of Opportunity. The taxonomy supplies the conceptual claim, and the graph supplies the empirical diagnosis of a field whose center of gravity is critical debate rather than foundational theory.

What would settle it

Rebuild the paper's quantitative diagnosis from an independently selected literature corpus with the list of papers and the rules for drawing links fixed in advance; if the resulting knowledge graph no longer shows Critique/Challenge concepts at the center and no longer shows a theory-practice gap, the structural-crisis claim collapses. For the facade claim, run a controlled benchmark comparing an LLM-based agent-framed pipeline against the same model used as a bare input-output tensor function on tasks marketed as 'agentic'; if the agentic framing consistently adds measurable capability, the claim that it only obscures mechanisms is weakened.

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Extended reading notes

Core claim

The central claim is that the agent-centric paradigm, especially in the current wave of LLM-based 'agentic AI', is operationally and conceptually misleading: what these systems compute is not agency but sequences of tensor transformations over high-dimensional embeddings, and describing them as agents with beliefs, plans, or intentions imposes an anthropocentric map onto a mathematical territory. The paper proposes replacing the default agent frame with a focus on agential systems—where intelligence is an emergent, distributed, system-level property—and on non-agentic computing, world models, continuous interaction, and material substrates as legitimate and possibly superior routes to general intelligence. It also claims, on the basis of its knowledge-graph analysis, that the field is in a structural crisis: critique of anthropocentrism is now more central to the discourse than the foundational agent concept, while theory and practice remain persistently disconnected.

Load-bearing premise

The paper's quantitative evidence for a structural crisis assumes that its self-built map of 98 concepts and the links it drew between them fairly represents the whole field; if that map is idiosyncratic, the claimed theory-practice gap loses its empirical support.

Editorial extensions

If this is right

  • LLM-based 'agentic' systems should be understood primarily as pattern-completion and tensor-transformation machines; agentic language remains a user-interface convenience, not an explanation of their operation.
  • Research funding and design effort would shift from building autonomous goal-seeking entities toward world models, continuous sensorimotor interaction, self-organization, and material or unconventional computing substrates.
  • The paper's Atlas of Opportunity identifies the most promising frontier as work that connects methods to critiques—for example, reinforcement-learning algorithms robust to Goodhart's Law, or formal verification of whether a neural architecture is computationally equivalent to an inferential algorithm.
  • Governance and accountability for AI would be reframed around verifiable system behavior and emergent properties rather than assumed intentions of an 'agent'.
  • The agent metaphor would be retained where it has heuristic value, such as human-AI interaction design, but dropped as the default ontology for intelligence research.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable corollary the paper leaves implicit: if the facade claim is right, then on a fixed benchmark an LLM-based system stripped of agentic scaffolding (no tool loop, no planning language, just direct input-output tensor computation) should match or approach the agent-framed version's performance; where it does not, the framing may be doing real engineering work.
  • The same knowledge-graph method could be applied to other contested concepts, such as 'intelligence', 'understanding', or 'alignment'; a symmetric finding—critique outweighing foundational theory—would suggest the pattern is general to fields in conceptual transition, not specific to agency.
  • The taxonomy's claim that agential systems are currently only biological implies that any future non-biological AGI would have to be either agentic (semi-autonomous, facade-like), non-agentic (a tool), or a new kind of agential system; the paper does not say which it expects, but the distinction sets up that question.
  • If anthropomorphism is mainly a product of interface design and marketing, as the paper suggests, then the agentic framing of consumer AI could be decoupled from the underlying engineering without changing performance—an economic and regulatory lever the paper mentions but does not develop.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper argues that the 'agent' paradigm, especially as applied to LLM-based systems, is a limiting framework for next-generation AI. It introduces a trichotomy of 'agentic' (semi-autonomous AI with an appearance of agency), 'agential' (fully autonomous, self-producing biological systems), and 'non-agentic' (tools without the impression of agency) systems, and it reviews conceptual ambiguities and anthropocentric biases in agent definitions, with Active Inference and LLM-based agents as recurring case studies. The paper proposes an alternative research agenda centered on system-level dynamics, world models, embodied and material intelligence, and agential systems. To support this, Section V presents a knowledge-graph analysis of 98 concepts from the authors' literature review, reporting category-level influence, temporal trends, innovation-strategy quadrants, and an 'Atlas of Opportunity' heatmap. The conclusion states that the field is undergoing a 'structural crisis' characterized by a persistent theory-practice gap, and that critiques of anthropocentrism are now more influential than the foundational agent concept itself.

Significance. The conceptual argument is coherent and well-grounded in a broad literature, including external critiques by Jaeger and Shanahan, and it has practical value as a provocation to reconsider agent-centric assumptions in LLM-based systems. The paper's distinction between agentic and agential systems, while admittedly difficult to operationalize, is a useful framing device for a debate that is often muddled. The 'Atlas of Opportunity' offers concrete, if illustrative, research directions at the method-critique and application-critique interfaces. However, the paper's strongest empirical claim—that the field exhibits a 'structural crisis' and a persistent theory-practice gap—rests entirely on a non-reproducible, unvalidated knowledge graph constructed with the authors' own tool and taxonomy. As it stands, the quantitative diagnosis is best read as an illustration of the authors' framework rather than an independent empirical finding. This significantly limits the current evidentiary weight of the paper, though the conceptual core remains defensible and worth publishing after substantial revision.

major comments (4)
  1. [Section V, Figures 1-4] The knowledge-graph analysis is not reproducible as reported, and it is load-bearing for the paper's central empirical claims of a 'structural crisis' and a 'persistent and stable gap' between theory and practice. The manuscript does not release the graph, does not specify inclusion criteria for the 98 concepts, does not describe the edge-construction protocol, and does not state who performed the six-category classification or how disagreements were resolved. The temporal analysis in Figure 2 requires per-period edges, but no period-assignment protocol is given; the influence-vs-interdisciplinarity analysis in Figure 3 requires directed or weighted connections whose nature is never specified. Without these details and without external validation, the 'strong empirical evidence' claimed in the Introduction and Section V is not supported.
  2. [Section V.A and Acknowledgments] The quantitative diagnosis risks circularity because the graph is built from the authors' own Discovery Engine tool and their own six-category taxonomy, and the node set and category assignments directly encode the paper's agentic/agential/non-agentic trichotomy. For example, the conclusion that 'Agential Systems' and 'systemic and emergent intelligence' are at the field's frontier follows in part from the authors choosing to include these as influential concepts in the graph. The finding that 'Critique/Challenge' concepts are increasingly central is similarly shaped by which critique concepts were selected and how they were connected. To make the diagnosis credible, the authors should provide an independent audit, an alternative taxonomy check, or a sensitivity analysis showing that the main conclusions are robust to node selection, category assignment, and edge-construction choices.
  3. [Section V.D (Atlas of Opportunity)] The headline evidence scores of 52 and 45 are counts of shared third-party concepts between pairs of categories, so they depend entirely on the manually constructed node set and category assignments. The paper presents these scores as a 'data-driven roadmap' without any null model, permutation test, or confidence interval, so it is unclear whether these frontiers are statistically meaningful or simply reflect the density of the authors' own concept selection. At minimum, the authors should report the raw contingency table and a permutation-based significance test, and they should temper the language that presents the Atlas as an objective empirical result.
  4. [Section III.A and Section VI] The paper asserts a 'structural crisis' in the conclusion, but the presented analysis only shows correlations within a self-constructed graph; it does not establish that the field is 'under strain' in any causal or structural sense. The conceptual arguments in Sections II-IV are plausible, but the quantitative evidence is not strong enough to move the conclusion from a programmatic position piece to an empirically established diagnosis. The authors should either substantially strengthen the empirical support (data release, validation, sensitivity analysis) or explicitly reframe the conclusion as a hypothesis-illustrating exercise rather than a confirmed finding.
minor comments (5)
  1. [Section IV.C] The headings contain typographical spacing errors: 'Chesterton's F ence' and 'Ashby's Law (Requisite V ariety)' should be 'Chesterton's Fence' and 'Ashby's Law (Requisite Variety)'.
  2. [References [84] and [85]] References [84] and [85] are exact duplicates of the same Chan et al. paper; one should be removed and the in-text citations renumbered accordingly.
  3. [Section V.A and V.C] The text refers to colors in Figures 1 and 3 (red, orange, green) and to 'Generative Crossroads,' 'Bridging Niches,' and 'Established Cores' quadrants, but the figures are not included in the manuscript text provided, and the quadrant thresholds are not defined; please add the figures or provide a precise description of the axes and color legend.
  4. [Abstract and Section V] The phrase 'systematic review' is used without specifying a review protocol (e.g., database search, screening criteria, or number of sources screened); either describe the protocol or replace 'systematic' with 'literature-based'.
  5. [Section IV.A] In the sentence beginning 'Rather than being an exclusive attribute of discrete agents, such internal representations” can be seen...', the opening quotation mark is missing or mismatched; please correct the quotation formatting.

Circularity Check

2 steps flagged · score 4.0 of 10

The conceptual critique is independently grounded, but the quantitative "diagnosis" rests on a self-constructed, non-released knowledge graph built with the authors' own Discovery Engine and the authors' own taxonomy, so part of the empirical confirmation is circular.

  1. self citation load bearing [Acknowledgments and Section V (Quantitative Diagnosis)]
    "This work was performed with the use of Discovery Engine, https://discovery.synthetix.institute/ for literature processing, structuring contributions, finding concept overlaps and summarizing according to procedure explained in [60]."

    The paper's quantitative diagnosis of a "structural crisis" and a "persistent and stable gap" is carried entirely by the 98-concept knowledge graph of Section V. The graph's construction procedure is delegated to Discovery Engine, the authors' own tool, described in ref [60], which is co-authored by Baulin. No data, inclusion criteria, edge-construction protocol, or external benchmark is supplied in this paper; the citation to [60] is thus the load-bearing support for the empirical claims. This is not independent evidence: the tool's output is accepted on the authority of the authors' own prior work, and the diagnosis cannot be checked or falsified from the present text.

  2. self definitional [Section III.A and Section V.A]
    "we constructed and analyzed a knowledge graph derived from the systematic literature review underpinning this paper. This graph consists of 98 concepts classified into six categories: Theoretical Concept, Architecture/Model, Entity/System, Method/Technique, Application/Domain, and Critique/Challenge. ... This proposed conceptual shift from 'agents' to 'agential systems' aligns with our quantitative findings, which show that concepts of systemic and emergent intelligence are located at the most dynamic and interdisciplinary frontiers of current research."

    The "quantitative findings" used to validate the paper's proposed shift to 'agential systems' are generated from a graph whose nodes and six categories were selected and classified by the authors from the literature review underpinning the paper. 'Agential Systems' is itself one of the graph's concepts, so its placement at the dynamic, interdisciplinary frontier is an output of the authors' own conceptual scheme and node choices, not an independent measurement of the field. The conclusion that systemic and emergent intelligence are at the frontiers restates, in quantitative form, the very framework the paper set out to establish; the graph provides no external benchmark that could disconfirm this placement.

full rationale

The central conceptual argument that agent-centric framing is limiting is not circular: it is supported by external critiques of LLM agency, active inference, and anthropocentrism, and by independent literature on complex systems and material intelligence. No equations are recycled, and no fitted parameter is renamed as a prediction. The circularity is localized to the quantitative confirmation layer: the Section V knowledge graph is constructed from the authors' own literature review, categorized with the authors' own six-concept taxonomy, and built with the authors' own Discovery Engine tool, yet it is then cited as strong empirical evidence for the paper's thesis. Because the graph is not released, and no inclusion criteria or edge-construction protocol are given, the quantitative results cannot independently support field-level claims such as 'structural crisis' or 'persistent and stable gap.' This is partial circularity, not total: the conceptual critique would stand on its own, but the empirical diagnosis is substantially self-confirming. Score 4 reflects that the central claim has independent content while the quantitative evidence is partly circular.

Assumptions & free parameters 0 free parameters · 5 assumptions · 1 invented entities

The central argument does not depend on fitted numeric parameters, but it does depend on several unvalidated assumptions: the representativeness of the authors' knowledge graph, the use of PageRank as influence, and the meaningfulness of the proposed taxonomy. The 'agential systems' concept is an invented category without independent evidence. The reliance on the authors' own Discovery Engine tool for the quantitative analysis adds a circularity risk.

assumptions (5)
  • domain assumption The 98-concept knowledge graph with six concept categories is a representative map of the field's intellectual structure.
    Section V states the graph was built from 'the systematic literature review underpinning this paper', but gives no inclusion criteria, inter-rater validation, or comparison to existing bibliometric databases.
  • domain assumption PageRank centrality and connection entropy quantify scholarly influence and interdisciplinarity.
    Sections V.A and V.C equate PageRank with influence and Shannon entropy with interdisciplinarity without justification that these graph metrics correspond to intellectual importance.
  • ad hoc to paper The agentic/agential/non-agentic trichotomy is a meaningful and operational classification.
    Section I introduces these categories, but no operational criteria are given that would allow independent classification of real systems; the boundary between 'agentic' and 'agential' remains qualitative.
  • domain assumption LLMs can be accurately described as tensor transformation machines whose apparent agency is a facade.
    Section II.C asserts this with citations to mechanistic and philosophical work, but the paper does not derive it from the architecture or training dynamics of LLMs.
  • domain assumption The 'Matter computes' hypothesis and material intelligence are viable foundations for general intelligence.
    Section IV.B relies on refs [38,41,49,50] for the claim that physical substrate can perform computation sufficient for intelligence; this remains a speculative research program.
invented entities (1)
  • Agential systems (fully autonomous, self-producing systems)
    purpose: This category is proposed as a non-anthropomorphic alternative to agentic AI, intended to capture biology-like autonomy in future intelligent systems.
    The paper defines 'agential systems' only by contrast with 'agentic' and 'non-agentic' systems, and gives no operational criteria or examples beyond biology. No falsifiable prediction or measurement is attached to the concept.

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Cite this review

Pith. "Pith review of Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?." pith.science (2026). https://pith.science/paper/WKL6IU4Q

@misc{pith2026250910875,
  author       = {Pith},
  title        = {Pith review of: Is the `Agent' Paradigm a Limiting Framework for Next-Generation Intelligent Systems?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WKL6IU4Q}},
  note         = {Machine review of arXiv:2509.10875}
}
read the original abstract

The concept of the 'agent' has profoundly shaped Artificial Intelligence (AI) research, guiding development from foundational theories to contemporary applications like Large Language Model (LLM)-based systems. This paper critically re-evaluates the necessity and optimality of this agent-centric paradigm. We argue that its persistent conceptual ambiguities and inherent anthropocentric biases may represent a limiting framework. We distinguish between agentic systems (AI inspired by agency, often semi-autonomous, e.g., LLM-based agents), agential systems (fully autonomous, self-producing systems, currently only biological), and non-agentic systems (tools without the impression of agency). Our analysis, based on a systematic review of relevant literature, deconstructs the agent paradigm across various AI frameworks, highlighting challenges in defining and measuring properties like autonomy and goal-directedness. We argue that the 'agentic' framing of many AI systems, while heuristically useful, can be misleading and may obscure the underlying computational mechanisms, particularly in Large Language Models (LLMs). As an alternative, we propose a shift in focus towards frameworks grounded in system-level dynamics, world modeling, and material intelligence. We conclude that investigating non-agentic and systemic frameworks, inspired by complex systems, biology, and unconventional computing, is essential for advancing towards robust, scalable, and potentially non-anthropomorphic forms of general intelligence. This requires not only new architectures but also a fundamental reconsideration of our understanding of intelligence itself, moving beyond the agent metaphor.

Figures

Figures reproduced from arXiv: 2509.10875 by the authors.

Figure 1
Figure 1. The Conceptual Landscape of Agentic AI. A force-directed layout of the 98-concept knowledge graph. Node size is scaled by PageRank (influence), and color indicates category. The central cluster is dominated by ‘Architecture/Model’ (e.g., LLM-based Agents), ‘Entity/System’ (e.g., Agentic Systems), and a high density of ‘Critique/Challenge’ and ‘Applica￾tion/Domain’ concepts, indicating a field driven by the implement… view at source ↗
Figure 2
Figure 2. Temporal Evolution: Average Influence of Concept Categories. This plot tracks the average PageR￾ank of concepts within each category across three time pe￾riods based on the literature review. The data shows that ‘Entity/System’ and ‘Critique/Challenge’ concepts have re￾mained the most influential categories over time, while the influence of specific ‘Architecture/Model’ concepts has waned. This indicates a paradigm … view at source ↗
Figure 3
Figure 3. Innovation Strategies: Influence vs. Interdisciplinarity. Each concept is plotted by its influence (PageRank centrality) and its interdisciplinarity (Shannon entropy of its connections across the six categories). Bubble size also reflects influence. The analysis reveals a dynamic core at the top right, where practical implementations (LLM-based Agents) and conceptual debates (Agentic Systems, Anthropocentrism in AI)… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The Atlas of Opportunity: Untapped Inno￾vation Frontiers. This heatmap shows the total evidence score for potential but currently non-existent links between concept categories. Brighter cells indicate a higher number of shared neighbors between nodes of the two categor…

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.