Contemporary Agent Technology: LLM-Driven Advancements vs Classic Multi-Agent Systems

Amelia B\u{a}dic\u{a}; Costin B\u{a}dic\u{a}; Dan Seli\c{s}teanu; Marcin Paprzycki; Maria Ganzha; Mirjana Ivanovi\'c; Zofia Wrona

REVIEW 3 major objections 4 minor 70 references

Contemporary Agent Technology: LLM-Driven Advancements vs Classic Multi-Agent Systems

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This review argues that many LLM-powered "multi-agent" systems are better described as distributed LLM applications than as true multi-agent systems, and that the field should build on classic MAS theory rather than ignore it.

desk verdict A competent but under-defended reflection on LLM agents vs classic MAS; the comparison table is useful, but the central 'not true MAS' claim never decides whether it is empirical or definitional. read the letter →

arxiv 2509.02515 v1 pith:G6CDZPCD submitted 2025-09-02 cs.MA cs.AI

Costin Bădică , Amelia Bădică , Maria Ganzha , Mirjana Ivanović , Marcin Paprzycki , Dan Selişteanu , Zofia Wrona This is my paper · ORCID

classification cs.MAcs.AI

keywords multi-agentsystemslargelanguagemodelsagenticAIBDIarchitectureagentcommunicationLLMagentsnormemergence

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

The paper argues that the rapid wave of LLM-powered agent systems is replaying problems that classic multi-agent systems (MAS) research already solved, and that many systems branded "MAS LLMs" are better described as distributed LLM applications than as multi-agent systems in the classical sense. The authors map the architectural pillars of modern LLM agents—profile, memory, planning, action, communication, perception, and learning—onto the foundational MAS concepts of autonomy, BDI reasoning, agent communication languages, artifacts, coordination, cooperation, negotiation, and norms. Their aim is to show where LLM agents genuinely extend the classic framework and where they quietly drop its guarantees, so that current innovation can build on rather than ignore decades of MAS theory. A sympathetic reader would take the paper as a call for theoretical grounding and careful terminology, not as a rejection of LLM agents.

What carries the argument

The comparative apparatus is the classic MAS characterization itself: the core properties of autonomy, reactivity, pro-activeness and social ability; the BDI model of beliefs, desires and intentions; the agents-and-artifacts view of the environment; formal agent communication based on speech act theory; and the classic treatments of coordination, cooperation, negotiation and norms. Against this yardstick the paper places the LLM-agent scaffolding stack—profile, memory (short-term, long-term, retrieval-augmented), planning, action via tools and the Model Context Protocol, natural-language communication, multimodal perception, and learning via human feedback—and asks, for each pair, what is preserved, what is loosened, and what is lost. The machinery makes the "true MAS vs distributed LLM app" distinction concrete: formal verifiability, predictability and explicit intent are traded for flexibility, emergence and implicit intent.

What would settle it

A concrete result that would weaken the paper's central claim is a benchmark in which LLM-agent teams without BDI or ACL scaffolding reliably satisfy formal coordination and commitment guarantees—for example, completing a shared plan with deadline and resource constraints across repeated trials—matching or exceeding classic MAS. Alternatively, a demonstration that LLM agents trained for inter-agent interaction, rather than user response, exhibit stable social abilities would undercut the claim that they lack genuine social interaction.

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Extended reading notes

Core claim

The paper's central claim is that contemporary LLM-driven systems, for all their power, frequently lack the core properties that define a true multi-agent system in the classic literature: genuine autonomy, robust social interaction, formal communication semantics, and verifiable norms. The authors argue that many current frameworks are sophisticated distributed LLM applications—orchestrated prompts, tool calls, and role assignments—rather than MAS in the classical sense, citing concerns from the MAS community that LLM agents are fine-tuned as single agents responding to users rather than trained for interaction with other agents. At the same time, they hold that classic concepts such as BDI, the artifacts view of the environment, speech act theory, and norm-based social order are not obsolete: they are being re-interpreted through natural language, with LLMs enriching beliefs, generating plans dynamically, and enabling negotiation through persuasive dialogue. The conclusion is an evolution of implementation, not an invalidation of the foundational concepts.

Load-bearing premise

The argument assumes that the classic MAS criteria—autonomy, social ability, formal communication, BDI-style commitment, and verifiable norms—are the right and complete standard for judging LLM agents, so that deviations from them count as deficiencies rather than as a genuinely new form of agency.

Editorial extensions

If this is right

Systems labeled "MAS LLMs" should be expected to demonstrate genuine autonomy and social interaction, not just role-played conversations, before the label is applied.
Tool-access standards like the Model Context Protocol will not by themselves restore MAS-level guarantees; they standardize tool calls but not the semantics of inter-agent communication.
BDI-inspired hybrid architectures point to a path where the BDI reasoning cycle supplies structure and the LLM supplies interpretation.
Formal communication may return in the form of context-adaptive meta-protocols rather than fixed agent communication languages.
Hybrid systems combining rule-based or BDI agents with LLM agents are a likely direction for robustness.

Reading between the lines

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

If the paper's yardstick is accepted, a testable program follows: benchmark LLM-agent teams specifically on formal coordination guarantees, commitment to intentions, and norm compliance rather than on task completion alone.
The authors' distinction suggests an empirical prediction: LLM-agent systems will show brittle performance precisely in settings that require reliable inter-agent commitments, such as long-horizon cooperative plans with deadlines.
The natural-language turn could be read not as a loss of social ability but as a new kind of social ability whose failure modes—ambiguity, prompt injection, hallucinated commitments—need their own theory; the paper gestures at this but leaves it undeveloped.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Two things to know about arXiv:2509.02515. First, it is not a research paper; it is a reflection from a group of senior MAS people, and its value is as a framing document. Second, its central warning—that many LLM-based systems are "sophisticated distributed LLM applications rather than true MAS"—is real but under-defended, and the paper never decides whether it is making an empirical claim or a definitional one.

What the paper does well: it gives a compact, readable mapping of where LLM agents sit relative to classic MAS concepts: BDI, A&A, ACLs, coordination, norms. Table 1 is genuinely useful as a teaching artifact. The sections on A&A vs. tool use/MCP and on formal ACLs vs. natural-language dialogue are the strongest; they name concrete systems (NatBDI, ChatBDI, JaCaMo, CrewAI) and make specific engineering observations that are easy to miss in the survey literature. The authors also cite the existing position papers (La Malfa et al., Botti) rather than pretending the critique is theirs alone.

Where it is soft: the load-bearing assumption is that the classical criteria (autonomy, social ability, BDI, formal ACLs, explicit norms) are the correct yardstick. That is asserted, not argued. If the yardstick is just one possible definition, then the claim that LLM systems are "not true MAS" is a terminological preference, not a deficiency. The paper occasionally acknowledges this—Section 3.2 says LLM mental states are secondary to its purposes, and Section 3.5 concedes experimental support for emergent cooperation is lacking—but it never steps back to say what empirical observation would count as genuine autonomy or sociality in an LLM system. The stress-test note is right about the oscillation.

Also, citation hygiene: [34] is used twice (Bratman and Ichida et al.), [46] is used twice, and [64] in the text refers to CrewAI but the reference list has API-Bank there. For a survey, that is a moderate but real problem; it undermines trust in the other references.

Who is this for? Someone new to agentic AI who wants the classical MAS perspective in one place, or a MAS researcher who wants a quick comparison table. It is not a breakthrough, and the authors don't claim one. But it is a competent reflection from people who know the field.

My recommendation: it deserves peer review, not desk rejection. Send it to a venue that handles position papers, and ask the authors to fix the references and either defend the classical yardstick or reframe the "not true MAS" claim as one possible lens rather than a verdict.

Referee Report

3 major / 4 minor

Summary. This paper offers a wide-ranging reflection comparing LLM-driven agent systems with classical multi-agent systems. It surveys the architectural building blocks of contemporary LLM agents (profiles, memory, planning, action, communication, perception, learning) and then systematically re-examines classical MAS concepts through the LLM lens: core agent properties, the BDI model, agents-and-artifacts versus tool use, speech act theory versus LLM-mediated dialogue, coordination/cooperation/negotiation as emergent behavior, and norms versus implicit social laws. The central thesis is that many contemporary "MAS LLMs" are better described as sophisticated distributed LLM applications than as true MAS in the classical sense, and that the field should build on rather than ignore foundational MAS knowledge. The paper closes with challenges, limitations, and future research directions.

Significance. If its central thesis is accepted, the paper provides a useful integrative perspective that could help align LLM-agent research with decades of MAS theory. Its strengths are a broad and structured comparison, a balanced treatment that acknowledges both continuity and transformation, and an unusually candid limitations section. The paper is not a technical contribution—it contains no experiments, derivations, or formal models—but it can serve as an orientation map for researchers entering the area. It explicitly hedges many empirical claims and cites recent primary sources. Its primary value is as a position or reflection piece, and its success depends on whether the reader accepts the classical MAS yardstick as the appropriate basis for comparison. The paper would be strengthened substantially by separating definitional claims from empirical claims and by specifying what would count as evidence for or against its main warning.

major comments (3)

[Section 3, opening paragraph; Table 1] Table 1 lists "Autonomy, Reactivity, Pro-activeness, Social ability" as "Core properties" shared by both Classic MAS and LLM-based agents, but Section 3.1 argues that LLM agents' autonomy is complicated by prompt dependence and non-determinism, and it cites reference [5] for the claim that many MAS LLM implementations lack genuine autonomy and sophisticated social interaction. This is a direct internal tension on a point that is central to the paper's thesis. The table should distinguish "properties claimed by system designers" from "classical definitional properties," or the text of Section 3.1 should be revised to be consistent with the table.
[Section 3, opening paragraph; Section 3.5] The claim that many systems "might be more accurately described as sophisticated distributed LLM applications rather than true MAS in the classical sense" is never operationalized with a falsifiable criterion. The paper oscillates between an empirical deficiency claim (systems lack genuine autonomy, social ability, or strategic reasoning) and a definitional claim (systems are not structured according to classical MAS architectures such as BDI or FIPA-ACL). These two readings require different evidence and have different consequences. I would ask the authors to specify a minimal, testable set of criteria for "true MAS"—for example, goal autonomy, explicit commitment or norm representation, and interaction governed by a protocol with defined semantics—and to indicate how the surveyed systems map to that test. Without such a test, the "reinventing the wheel" warning risks reducing to a terminological preference rather than a substantive concern.
[Section 3.5, Cooperation subsection] The sentence "However, experimental support that this will really happen (and will happen every time) is lacking" is an honest but damaging admission for the paper's earlier assertion that LLM-agent cooperation is an emergent, dialogue-driven behavior. The section then bases its positive claims on a small number of studies—for coordination, reference [38] (LLM-Coordination); for cooperation, reference [46] on Diner's Dilemma; for negotiation, the same reference number [46] on bargaining—each a single experimental setup with limited generality. To keep the reflective claim defensible, the authors should present these as preliminary hypotheses with explicit scope limitations, or they should gather a broader set of corroborating studies before generalizing to "LLM agents" as a class.

minor comments (4)

[References] Reference numbers are duplicated: [34] is used both for Bratman's book and for the NatBDI paper by Ichida et al., and [46] is used both for the bargaining paper by Oh et al. and for the Diner's Dilemma paper by Warnakulasuriya et al. This creates ambiguity in the citation trail and should be corrected by renumbering.
[Section 2.3, first paragraph] The sentence "Note that similar considerations were made for earlier classic MAS architectures" would benefit from a citation to one or two classic MAS surveys so that readers can locate the claimed precedent.
[Section 3.4, last paragraph before the Agora discussion] The phrase "building a new kind of wheel" is a helpful nuance, but it partially undercuts the introduction's "reinventing the wheel" warning (Section 1). The authors should explicitly reconcile these two framings, for example by stating that the risk is reinventing the wheel for problems already solved (coordination, commitment, norm enforcement) even if the implementation medium is genuinely new.
[References] Reference [63] is a Medium blog post rather than a peer-reviewed source; consider replacing it with the CrewAI documentation or a peer-reviewed description of the framework if one is available.

Circularity Check

0 steps flagged · score 2.0 of 10

No material circularity: the survey's central comparison rests on external definitions and critique, with author self-citations confined to non-load-bearing context.

full rationale

This paper is a reflective survey, not a derivation; there are no fitted parameters, equations, or predictions to reduce. The central comparison between LLM agents and classic MAS is anchored in external sources: Wooldridge's textbook [41] supplies the classic agent properties, and the 'missing the mark' critique is attributed to La Malfa et al. [5], which is not authored by the present group. The authors' own earlier work appears only in contextual claims: [1,2] for the historical foundations of MAS, and [52,53,60] for the existence and engineering status of agent platforms and for JADE message efficiency. None of these self-citations carries the paper's thesis; if the cited surveys were wrong, the paper's argument about BDI, communication, norms, and emergent coordination would still stand on its cited external literature and examples. The paper also explicitly flags its own limitations ('experimental support that this will really happen (and will happen every time) is lacking') and defers the mental-state question as 'secondary to this work,' which shows that it is not presenting a forced or self-justifying empirical claim. The concern raised by a skeptical reader—that the classic-MAS yardstick is one possible definition rather than an empirically established standard—is a substantive critique of the paper's framing, but it is a correctness/positional matter, not circularity in the derivation-chain sense. Accordingly, no circular step can be exhibited, and the appropriate score is low.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new parameters or entities. It relies on standard domain assumptions from MAS literature and on the reliability of its cited empirical studies.

assumptions (3)

domain assumption Classic MAS concepts (autonomy, BDI, coordination, norms) are the appropriate framework for analyzing LLM-based agents.
The entire comparison in Sections 3.1 to 3.6 assumes that these classical notions are the right lens, and treats deviations as shortcomings.
domain assumption LLMs do not possess beliefs or desires in the human sense.
Stated in Section 3.2, this philosophical assumption is used to argue that BDI is being re-interpreted rather than instantiated.
domain assumption The cited empirical studies of LLM-agent societies (e.g., [46], [47]) are reliable.
Claims about emergent norms, cooperation, and negotiation rest entirely on a small number of cited papers; no independent replication is offered.

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Cite this review

Pith. "Pith review of Contemporary Agent Technology: LLM-Driven Advancements vs Classic Multi-Agent Systems." pith.science (2026). https://pith.science/paper/G6CDZPCD

@misc{pith2026250902515,
  author       = {Pith},
  title        = {Pith review of: Contemporary Agent Technology: LLM-Driven Advancements vs Classic Multi-Agent Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/G6CDZPCD}},
  note         = {Machine review of arXiv:2509.02515}
}

read the original abstract

This contribution provides our comprehensive reflection on the contemporary agent technology, with a particular focus on the advancements driven by Large Language Models (LLM) vs classic Multi-Agent Systems (MAS). It delves into the models, approaches, and characteristics that define these new systems. The paper emphasizes the critical analysis of how the recent developments relate to the foundational MAS, as articulated in the core academic literature. Finally, it identifies key challenges and promising future directions in this rapidly evolving domain.

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