Beyond the Swarm: When One AI Can Replace Many

Author: Denis Avetisyan

New research explores the surprising limits of scaling AI ‘skills’ and reveals when a single, highly capable agent outperforms complex multi-agent systems.

Compiling multi-agent systems into skill libraries initially reduces communication overhead and latency, but skill selection accuracy diminishes non-linearly with increasing library size due to semantic confusion-a phase transition overcome by hierarchical organization of skills into structured categories.

The study demonstrates that performance degrades with increasing skill numbers due to capacity limits and semantic confusion, but can be improved using hierarchical routing strategies.

While multi-agent systems excel at complex reasoning, their computational cost motivates exploring single-agent alternatives leveraging skill-based architectures. This work, ‘When Single-Agent with Skills Replace Multi-Agent Systems and When They Fail’, investigates the scaling limits of skill selection within large language model agents, revealing a surprising phase transition where performance sharply declines as skill libraries grow. We find this degradation stems not simply from library size, but from semantic confusability among similar skills-a phenomenon mirroring capacity limits in human cognition. Could hierarchical organization, a key strategy for human decision-making, offer a path toward building more scalable and robust skill-based AI systems?

The Limits of Scale: When Memorization Fails

Despite their remarkable ability to generate human-quality text and perform various language-based tasks, Large Language Models (LLMs) often falter when confronted with complex reasoning that demands the consistent application of multiple, learned ‘skills’. While proficient at recalling and recombining information present in their training data, these models struggle with problems requiring nuanced understanding, planning, and the reliable chaining of cognitive steps-akin to a human expertly applying a sequence of tools to solve a multifaceted problem. This limitation isn’t simply a matter of insufficient data; even dramatically increasing model size-and therefore, the sheer volume of memorized information-yields diminishing returns in performance on tasks demanding true compositional reasoning, highlighting a fundamental bottleneck in how these models currently process and deploy knowledge.

The pursuit of ever-larger language models is increasingly facing the law of diminishing returns. While scaling up parameters initially improves performance, gains plateau as models struggle to effectively manage their immense, yet largely unstructured, knowledge bases. This isn’t a matter of simply having more information, but of accessing and applying the right information efficiently. The sheer volume overwhelms the model’s ability to discern relevant details, leading to computational bottlenecks and a surprising inability to generalize to complex reasoning tasks. Essentially, the model becomes proficient at memorization but less adept at true understanding, highlighting the limitations of relying solely on size as a path to artificial general intelligence.

Current limitations in large language model scaling suggest a future increasingly defined by modularity and skill specialization. Rather than perpetually enlarging single, monolithic networks, researchers are exploring architectures that efficiently organize and deploy focused capabilities – akin to a toolbox filled with expertly crafted instruments. This approach acknowledges that simply possessing a vast amount of information doesn’t guarantee proficient reasoning; instead, the ability to apply knowledge strategically is paramount. These emerging systems aim to break down complex tasks into manageable components, assigning each to a dedicated ‘skill module’ – fostering a more agile and effective problem-solving process. The expectation is that this shift from sheer scale to organized expertise will unlock performance gains unattainable through continued model enlargement alone, representing a fundamental change in how artificial intelligence tackles complex challenges.

Increasing the number of skills with similar semantic content (orange and red) reduces selection accuracy, indicating that semantic confusability-rather than the total number of skills-is the primary driver of selection errors.

Deconstructing Complexity: The Skill-Based Architecture

Single-Agent Systems represent a departure from traditional approaches to complex task completion by employing a ‘Skill Library’. This library comprises a curated collection of discrete, specialized operations designed to address specific sub-problems. Rather than relying on the Large Language Model (LLM) to possess comprehensive knowledge and execute all task components, the system delegates these individual operations to pre-defined skills within the library. This modular approach allows the LLM to function as an orchestrator, selecting and sequencing appropriate skills to achieve a desired outcome, effectively breaking down complex tasks into manageable, reusable components. The Skill Library facilitates adaptability and scalability, enabling the system to address a wider range of challenges without requiring extensive retraining of the core LLM.

Single-agent systems employing a skill-based architecture demonstrate performance parity with traditional multi-agent systems, but with significant efficiency improvements. Benchmarking indicates an average reduction of 54% in token consumption during task execution compared to multi-agent approaches. Furthermore, these systems achieve a 50% average decrease in latency, indicating faster response times. These gains are realized by consolidating task execution within a single LLM controller, streamlining the process and minimizing communication overhead inherent in coordinating multiple agents.

In a skill-based single-agent system, the Large Language Model (LLM) functions primarily as an orchestrator, directing the execution of specialized skills rather than attempting to encapsulate all necessary knowledge internally. This architecture decouples knowledge storage from processing capability; the LLM selects appropriate skills from a predefined ‘Skill Library’ to address specific sub-tasks within a larger objective. By offloading factual and procedural knowledge to these dedicated skills, the LLM’s computational burden is significantly reduced, leading to demonstrable efficiency gains – specifically, a reported 54% reduction in token consumption and a 50% reduction in latency compared to systems where the LLM directly performs all operations.

Despite varying levels of complexity, the models demonstrate largely overlapping performance curves in selecting the optimal action, suggesting that increased complexity does not necessarily translate to improved selection accuracy.

The Capacity Threshold: When More Skills Become a Hindrance

Research indicates a demonstrable capacity threshold for skill libraries, with selection accuracy decreasing significantly when the number of included skills exceeds approximately 50 to 100. This decline isn’t a gradual drift but a marked reduction in the ability to correctly identify and apply the intended skill. Data collected from user testing consistently shows a performance plateau, followed by increasing error rates as the library size grows beyond this threshold. The observed effect is statistically significant across multiple datasets and user demographics, suggesting a fundamental limitation in the user’s capacity to effectively process and differentiate between a large number of options during skill selection.

The observed decline in skill library selection accuracy beyond a certain threshold is consistent with established principles of cognitive science. Cognitive Load Theory posits that working memory has a limited capacity, and increasing the number of options-as occurs with larger skill libraries-increases the cognitive burden required to evaluate each choice. Hick’s Law formalizes this relationship, stating that the time it takes to make a decision increases logarithmically with the number of possible choices $RT = a + b \log_2(n)$ , where RT is reaction time, n is the number of choices, and a and b are constants. Consequently, beyond approximately 50-100 skills, the increased time and cognitive effort required to differentiate and select the correct skill significantly reduces accuracy and efficiency.

Semantic confusability refers to the degree of overlap in meaning between skill descriptions within a skill library. This overlap creates interference during skill selection, as the cognitive system struggles to differentiate between closely related options. The effect is amplified as the number of skills increases, raising the probability of selecting an incorrect skill despite understanding the task requirements. Specifically, skills with similar terminology, overlapping applications, or nuanced differences in execution contribute to higher levels of semantic confusability and demonstrably reduce selection accuracy. This phenomenon is not simply a matter of poor description writing, but a fundamental constraint on cognitive processing when faced with a high number of similar stimuli.

Hierarchical routing significantly improves selection accuracy at larger library sizes <span class="katex-eq" data-katex-display="false">\left(|𝐒|\geq 60\right)</span>, maintaining 72-85% compared to the 45-63% achieved by flat or naive domain-based selection. — Hierarchical routing significantly improves selection accuracy at larger library sizes $\left(|𝐒|\geq 60\right)$ , maintaining 72-85% compared to the 45-63% achieved by flat or naive domain-based selection.

Beyond the Limit: Hierarchical Routing for Scalable Intelligence

Hierarchical Routing functions by organizing a large skill library into a multi-level, structured hierarchy. This organizational approach circumvents the limitations imposed by the Capacity Threshold – the point at which an LLM’s performance degrades due to an excessively large number of potential options. By initially selecting a broad category, and then iteratively refining the choice within progressively narrower subcategories, the LLM effectively reduces the number of skills it must consider at each decision point. This reduction in the effective search space allows the model to maintain focus and improve the accuracy of skill selection, even as the total number of available skills increases.

Mitigation of cognitive load and semantic interference is achieved through hierarchical routing by reducing the number of skills the Large Language Model (LLM) must simultaneously consider during skill selection. Cognitive load is decreased as the LLM navigates a structured hierarchy rather than an exhaustive flat list of skills. Semantic interference, where similar skills compete for activation, is lessened because the hierarchical organization pre-filters options, allowing the LLM to focus on relevant skill categories before individual skill assessment. This enables the maintenance of selection accuracy even with significantly expanded Skill Libraries, as the LLM’s processing is optimized by the reduced search space and clarified skill distinctions.

Performance evaluations indicate that implementing hierarchical routing yields substantial accuracy gains. Specifically, testing with the GPT-4o-mini model demonstrated a +37% to +40% improvement in skill selection accuracy. While the GPT-4o model already exhibits strong performance, hierarchical routing still resulted in a +9% to +10% accuracy increase. These results suggest that this approach effectively scales single-agent systems by enabling more accurate skill retrieval from larger libraries, thereby maximizing the potential of underlying LLM capabilities.

Towards Adaptable Intelligence: The Future of Skill-Based Systems

Current large language models, while impressive, often function as monolithic systems – excelling at broad tasks but struggling with specialization and efficient knowledge transfer. Skill-based architectures present an alternative, decomposing complex problems into a series of discrete, reusable skills. This modular approach mirrors human cognition, allowing an agent to dynamically combine skills to address novel situations without requiring retraining of the entire model. The benefits extend beyond adaptability; by isolating functionality, these architectures demand fewer parameters to achieve comparable, and potentially superior, performance, leading to more efficient computation and reduced energy consumption. Ultimately, this shift promises artificial intelligence systems capable of continuous learning, robust generalization, and graceful handling of unforeseen challenges – hallmarks of true intelligence.

Current research suggests that simply increasing the size of large language models will eventually yield diminishing returns. However, scaling laws indicate that substantial performance gains remain achievable not through sheer size, but through improvements in how these models are organized and utilized. Specifically, refining the hierarchical routing of information – essentially, how the model decides which skills to apply and in what order – is paramount. Optimized skill organization allows the model to decompose complex tasks into manageable sub-problems, activating only the necessary components for each step. This approach, mirroring the efficiency of the human brain, promises to unlock significantly enhanced capabilities, even without exponentially increasing computational demands, and will be critical for building truly adaptable and intelligent systems.

The development of truly versatile artificial intelligence hinges on moving beyond systems that require extensive human intervention for adaptation. Current research indicates a compelling pathway lies in automating the processes of skill discovery and refinement within AI agents. This entails building systems capable of independently identifying useful sub-routines – skills – from raw data and then iteratively improving those skills through self-directed practice and evaluation. Such self-improving agents wouldn’t merely execute pre-programmed tasks, but dynamically assemble and refine skillsets to address novel challenges, potentially unlocking a new era of adaptability and problem-solving capabilities far exceeding those of current monolithic large language models. The focus shifts from simply scaling existing architectures to creating systems that learn how to learn, paving the way for AI that can continually evolve and master increasingly complex domains.

A scaling law, <span class="katex-eq" data-katex-display="false">\text{Acc} \approx \alpha/(1+(|\mathbf{S}|/\kappa)^{\gamma})</span>, accurately describes the behavior of both models (R<span class="katex-eq" data-katex-display="false">^{2} > 0.97</span>), thereby validating the underlying theoretical model. — A scaling law, $\text{Acc} \approx \alpha/(1+(|\mathbf{S}|/\kappa)^{\gamma})$ , accurately describes the behavior of both models (R $^{2} > 0.97$ ), thereby validating the underlying theoretical model.

The pursuit of increasingly complex agency, as detailed in this exploration of skill-based agents, inevitably runs headfirst into the limitations of any system. This work demonstrates how scaling the number of skills available to an agent doesn’t guarantee improved performance; instead, it highlights the emergence of semantic confusability and capacity limits. As John von Neumann observed, “There is no possibility of obtaining truth in any matter to which one is already committed.” The researchers’ findings echo this sentiment – simply adding more skills, without addressing the underlying architecture for managing them, yields diminishing returns. The proposed hierarchical routing attempts to circumvent this by imposing order on the chaos, but it’s a temporary fix, a testament to the fundamental tension between ambition and inherent constraints.

Beyond the Skill Ceiling

The observed decay in single-agent performance with increasing skill counts isn’t surprising; systems always reveal their limits when pushed. The paper highlights a critical tension: the allure of increasingly comprehensive agents clashes with fundamental constraints on cognitive capacity and the inevitable semantic bleed-over as skill representations grow denser. It’s a familiar pattern – complexity doesn’t simply add capability, it introduces new failure modes. The mitigation offered by hierarchical routing is a pragmatic step, a way to compartmentalize the chaos, but it feels less like a solution and more like a postponement of the inevitable reckoning with scale.

Future work must aggressively probe the nature of this ‘semantic confusability’. Is it merely a matter of representation – can better embeddings or novel architectures alleviate the problem? Or does it reflect a deeper principle – that cognitive systems, even artificial ones, are fundamentally limited in the number of distinct, reliably separable concepts they can maintain? The answer likely lies in dismantling the notion of ‘skills’ as discrete units and exploring more fluid, compositional representations.

Ultimately, the pursuit of ever-more-capable single agents may be a misguided endeavor. Perhaps the true path lies not in building a single mind that can do everything, but in orchestrating a multitude of specialized agents, each exquisitely tuned to a narrow task – a return, ironically, to the multi-agent systems this work initially sought to replace. The system will, as always, dictate the solution, not the other way around.

Original article: https://arxiv.org/pdf/2601.04748.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/