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REVIEW 3 major objections 5 minor 1 cited by

AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This review paper argues that Agentic AI moves manufacturing from reactive task optimization to proactive, system-level intelligence, with the potential to make manufacturing ecosystems self-optimizing and continuously evolving.

desk verdict A clear manufacturing-centric taxonomy of AI agents, undermined by Section 5 claims that treat speculative capabilities as established facts. read the letter →

arxiv 2507.01376 v1 pith:DOVRUHLE submitted 2025-07-02 cs.AI

Yinwang Ren , Yangyang Liu , Tang Ji , Xun Xu This is my paper · ORCID
classification cs.AI
keywords AIAgentsAgenticGenerativeLargeLanguageModelsMultimodalLLMsSmartManufacturingAutonomousDecision-MakingRetrieval-AugmentedGeneration
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 review paper organizes the overlapping concepts of AI agents, LLM-Agents, MLLM-Agents, and Agentic AI into one evolutionary narrative, and claims the narrative has a direction: from task-optimizing tools to systems that set their own manufacturing goals. The paper argues that Agentic AI signals a move from reactive task optimization to proactive system-level intelligence, emphasizing autonomy, adaptability, system-wide coordination, and continuous learning. If true, future manufacturing would shift from fixed automation to autonomous, goal-driven decision-making across production, logistics, and enterprise management, with systems that redefine objectives as conditions change and learn continuously without periodic retraining. The paper also states that such systems remain in early stages, while cataloguing open challenges in document parsing, multimodal alignment, interpretability, workforce adoption, accountability, and return on investment.

What carries the argument

The load-bearing conceptual machinery is the staged evolutionary path of agent technology, from rule-based expert systems to AI-powered decision-making to LLM-Agents to MLLM-Agents to Agentic AI, together with two definitions that give the stages content. LLM-Agents are characterized by four modules, profiling, memory, planning, and action, which the paper takes from a survey of LLM-based autonomous agents. Agenticness is defined through four dimensions, goal complexity, environmental complexity, adaptability, and independent execution, taken from an industry governance source, and the paper treats it as a gradual spectrum rather than a binary class. The bridge from today's systems to the future is retrieval-augmented generation (RAG) and knowledge graphs, which ground the paper's cited manufacturing examples, such as a semiconductor virtual assistant, a conversational CNC monitoring system, and an MLLM-based ceramic tile quality-control system, in real plant data.

What would settle it

A controlled field trial in which an agent is given authority to re-plan production, substitute materials, and reconfigure logistics through a simulated supply-chain disruption, with every human takeover logged, would settle the central transition claim, because the Agentic AI stage is defined by independent execution and the claim fails if the agent cannot close the loop without supervision in most trials.

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Extended reading notes

Core claim

The central claim is that GenAI-enabled AI agents fall into LLM-Agents and MLLM-Agents, both of which remain task-oriented optimizers, while Agentic AI represents a qualitatively new stage defined by a spectrum of agenticness with four dimensions: goal complexity, environmental complexity, adaptability, and independent execution. Once these capabilities cross a threshold, the paper argues, agents transition from optimizing fixed schedules to autonomously defining, refining, and executing manufacturing goals in dynamic environments with minimal human intervention. In manufacturing this shift appears as four transitions: from task execution to goal-driven optimization, from rule-based control to adaptive planning, from localized optimization to system-level orchestration, and from static execution to continuous learning and evolution, giving Agentic AI the potential to transform manufacturing ecosystems into self-optimizing, continuously evolving systems.

Load-bearing premise

The central claim rests on assuming that today's retrieval-augmented and multimodal assistants will scale into fully autonomous, goal-formulating systems despite the unresolved technology challenges the paper lists, namely document parsing, multimodal alignment, and interpretability, because the paper's own examples only demonstrate the assistant stage.

Editorial extensions

If this is right

  • Manufacturers would move from fixed schedules to goal-driven optimization, with an agentic system dynamically resetting production objectives for throughput, energy, and resource allocation as market and supply-chain conditions shift.
  • On a supply-chain disruption, the system would autonomously modify production workflows, identify alternative materials, and reconfigure logistics without human oversight.
  • Optimization becomes system-wide, coordinating production, logistics, and enterprise management together, which the paper argues is crucial for high-mix, low-volume manufacturing.
  • AI systems would improve continuously through self-supervised and reinforcement learning instead of periodic retraining, reducing waste and energy use over extended operational cycles.
  • Deployment is gated by open problems the paper itself lists: cross-format document parsing, multimodal alignment, black-box interpretability, workforce resistance, accountability, and unclear return on investment.

Reading between the lines

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

  • The paper's evidence supports the assistant stage but not the autonomy stage: its cited systems are retrieval-augmented assistants, so the scaling from these to fully autonomous Agentic AI is an assumption rather than a demonstrated result.
  • The agenticness-as-spectrum definition invites a practical maturity rubric that ranks any manufacturing AI deployment along the four dimensions, telling a plant whether it is buying an assistant or an agent.
  • If system-level orchestration is the payoff, the natural benchmark is cross-functional: test an agent on a combined scheduling, logistics, and inventory disruption rather than on isolated tasks, because the paper's value claim is that siloed optimization underperforms orchestration.
  • The accountability and ROI discussion implies an economic gate left open, so a cost-benefit framework for decisions taken without human sign-off would be a direct next step.
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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

3 major / 5 minor

Summary. This paper is a survey-style review of AI agents and Agentic AI in the context of future manufacturing. It traces the evolution of AI from symbolic and connectionist paradigms to LLMs and MLLMs, then presents a staged account of GenAI-enabled AI agents (LLM-Agents, MLLM-Agents) and the emerging concept of Agentic AI. The review describes applications of such agents in manufacturing, including semantic retrieval, multimodal perception, adaptive learning, and autonomous decision-making, and it identifies technical, workforce, and accountability challenges. The central claim is that Agentic AI will shift manufacturing from reactive, rule-based task optimization to proactive, system-level, self-optimizing intelligence, with capabilities such as autonomous goal formulation, adaptive planning, system-wide orchestration, and continuous learning without human intervention.

Significance. The paper provides a useful, clearly structured taxonomy of concepts—LLM-Agents, MLLM-Agents, and Agentic AI—grounded in traceable external definitions (notably OpenAI's agenticness framework [41] and Gartner's trend report [40]) and in recent manufacturing applications such as IMVA, ChatCNC, and the ceramic-tile RAG system. The historical background and the cited examples are largely accurate and well-documented. The review could serve as a helpful entry point for researchers entering this area, especially because it consolidates scattered terminology and connects it to manufacturing. However, the paper's forward-looking claims in Section 5 are repeatedly phrased as established capabilities rather than as a research vision or hypothesis. That phrasing, if left unchanged, overstates the evidence base and weakens the paper's scientific credibility. The survey value is real; the speculative parts need to be explicitly framed as potential, not demonstrated.

major comments (3)
  1. [§5.2 and §5.4] The paper states in Section 5.2 that an Agentic AI system 'can autonomously modify production workflows, identify alternative materials, and reconfigure supply logistics without human oversight' and in Section 5.4 that it can 'continuously improve its models ... without human intervention.' These are presented as current or near-term capabilities. Yet Section 4.3 itself concedes that 'practical implementations remain in the early stages,' and the manufacturing examples cited in Sections 4.1–4.2 (IMVA, ChatCNC, ceramic-tile RAG) are retrieval, diagnostic, or report-generation assistants—none is shown to autonomously change production workflows or reconfigure supply logistics. The central claim of the paper therefore conflates potential value with demonstrated feasibility. This is load-bearing. I ask the authors to either provide concrete evidence or a carefully caveated roadmap for how today's assistants scale to the described autonomy, or to rewrite Sections 5.2 and 5.4 so that these are explicitly labeled as open research opportunities and speculative boundary scenarios, not established facts.
  2. [§3.3] The definition of agenticness as a spectrum is useful, but the paper adds that 'as these capabilities reach a sufficiently high threshold, AI agents naturally transition into Agentic AI systems' without specifying any threshold, operational metric, or evidence that current LLM/MLLM-based agents are moving toward that threshold. This makes the transition claim unfalsifiable in its current form. The paper should either propose concrete measurable dimensions (e.g., task completion rate, degree of human intervention, adaptability under distribution shift) or treat the trajectory to high agenticness as an explicitly open empirical question, linked to the challenges catalogued in Section 6. Without this, the continuity assumption from retrieval assistants to autonomous agents is unsupported.
  3. [§5.3] The claim of 'system-level orchestration' across production, logistics, and enterprise management is stated without a single cited implementation or prototype that demonstrates cross-subsystem autonomous coordination in manufacturing. Given the paper's own admission in Section 4.3 that implementations are early-stage, Section 5.3's assertions about autonomous synchronization of scheduling, inventory, and transportation should be reframed as a design goal or research direction, or be supported by relevant literature on multi-agent orchestration in other domains (e.g., traffic or finance) that the paper could discuss as existence proofs.
minor comments (5)
  1. [§3.2] The model name 'LLaV A' appears with a stray space; it should be 'LLaVA'.
  2. [§4.2] The citation 'Heredia ´Alvaro et al.' has a formatting issue with the surname; it should be written consistently as 'Heredia Álvaro et al.' or 'J. A. Heredia Álvaro'.
  3. [Abstract and Introduction] The phrase 'modern manufacturing' in the abstract and introduction is used broadly; the paper would benefit from a brief scoping statement about which manufacturing sectors and process types are included (e.g., discrete vs. process manufacturing, high-mix low-volume vs. mass production).
  4. [Figure 3] Figure 3 presents the evolutionary path from rule-based expert systems to Agentic AI, but the figure's annotation of the 'agentic threshold' is not explained in the text; please either remove or define it in Section 3.3.
  5. [§6.1.1] The section on cross-format document parsing is terse; adding one or two concrete examples of failure modes (e.g., scanned engineering drawings vs. text formulas) would make the challenge more tangible for a manufacturing audience.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: this is a narrative review whose central concepts are imported from external sources (Gartner, OpenAI) and whose self-citations are background references, not load-bearing derivations.

full rationale

The paper is a review, not an empirical derivation. It proposes no equations, fits no parameters, and derives no quantitative predictions from fitted inputs. Its central definitions and evolution claims rest on cited external sources: the agenticness definition comes from OpenAI [41], Gartner's trend identification from [40], and the LLM/MLLM agent architectures from external surveys [39]. The claimed transition 'from GenAI-enabled AI Agents to Agentic AI' is presented as a conceptual spectrum inherited from [41], not as a result derived within the paper. The only self-citations are [2] (Zheng et al., including author Xu) and [27] (Ren et al.), both used as background examples of smart-manufacturing frameworks or industrial ML applications; neither is invoked to justify the paper's central claims, and neither supplies a uniqueness theorem or an ansatz that the paper treats as forced. The paper itself concedes in Section 4.3 that 'practical implementations remain in the early stages' and in Section 5.4 that 'the full realization of Agentic AI remains an ongoing challenge,' which further shows the forward-looking statements are framed as potential rather than as demonstrated results. The reader's concern about an unstated continuity assumption is a correctness or evidence-strength issue, not a circularity issue: the paper does not define Agentic AI in terms of its own conclusions, nor does it rename a fitted quantity as a prediction. Therefore the appropriate circularity score is 0.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

As a review, the paper introduces no free parameters, fitted values, or new physical or conceptual entities. It relies on published literature for all definitions and examples, and its forward-looking claims rest on the domain assumptions listed above.

assumptions (4)
  • domain assumption The definition of agenticness from OpenAI (reference [41]) is accepted as the basis for positioning Agentic AI on a spectrum.
    Section 3.3 builds the entire Agentic AI concept on this external policy-paper definition, without academic peer review or independent validation.
  • domain assumption The cited application examples (IMVA [44], ChatCNC [45], ceramic defect RAG [46]) are correct and representative of GenAI-enabled agents in manufacturing.
    Section 4 uses these systems as evidence that GenAI agents already deliver value; if any is flawed or atypical, the capability claims weaken.
  • domain assumption Progress in MLLMs from adjacent fields (medical diagnosis [47], robotics [48]) will transfer to manufacturing contexts.
    Section 4.2 generalizes from medical and robotics MLLM results to manufacturing, assuming similar data and task conditions.
  • domain assumption The technology challenges listed in Section 6 are solvable within the envisioned timeline.
    The optimistic Section 5 picture of autonomous Agentic AI implicitly assumes that parsing, multimodal alignment, and interpretability barriers will be overcome, though the paper offers no solution or evidence.

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Cite this review

Pith. "Pith review of AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing." pith.science (2026). https://pith.science/paper/DOVRUHLE

@misc{pith2026250701376,
  author       = {Pith},
  title        = {Pith review of: AI Agents and Agentic AI-Navigating a Plethora of Concepts for Future Manufacturing},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DOVRUHLE}},
  note         = {Machine review of arXiv:2507.01376}
}
read the original abstract

AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments. With the rapid advancements in generative AI (GenAI), large language models (LLMs) and multimodal large language models (MLLMs) have significantly improved AI agents' capabilities in semantic comprehension, complex reasoning, and autonomous decision-making. At the same time, the rise of Agentic AI highlights adaptability and goal-directed autonomy in dynamic and complex environments. LLMs-based AI Agents (LLM-Agents), MLLMs-based AI Agents (MLLM-Agents), and Agentic AI contribute to expanding AI's capabilities in information processing, environmental perception, and autonomous decision-making, opening new avenues for smart manufacturing. However, the definitions, capability boundaries, and practical applications of these emerging AI paradigms in smart manufacturing remain unclear. To address this gap, this study systematically reviews the evolution of AI and AI agent technologies, examines the core concepts and technological advancements of LLM-Agents, MLLM-Agents, and Agentic AI, and explores their potential applications in and integration into manufacturing, along with the potential challenges they may face.

Figures

Figures reproduced from arXiv: 2507.01376 by the authors.

Figure 1
Figure 1. Relationship from AI to LLMs and MLLMs proximate complex, nonlinear functions in high-dimensional spaces renders it particularly effective for modelling the heterogeneous, data-intensive, and multivariable processes inherent in modern manufacturing systems [25]. ML methods, particularly DL, have been widely adopted in manufacturing, sup￾porting tasks such as predictive maintenance [26], process optimization [27], an… view at source ↗
Figure 2
Figure 2. Different components and its functions of LLM-Agents [39] [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Evolutionary Path of AI Agent Technologies [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.