AI Agents vs Large Models: Why Team-Based Approach Works Better Than Bigger Systems

For many years, the AI industry has focused on building larger language models (LLMs). This strategy delivered positive results. LLMs can now write complex code, solve math problems, and create compelling stories. The belief behind this strategy was that increasing data, computing power, and model parameters would improve performance. This concept is also supported by neural scaling laws. However, a new approach is gaining traction. Rather than developing a single large AI system to handle all tasks, researchers are now focusing on creating teams of smaller, specialized AI agents that work together. This article explores how the team-based approach offers greater efficiency, flexibility, and potential to surpass the performance of traditional large models.

Problems with Large Models

While large language models (LLMs) have achieved remarkable results, continuing to scale them up is becoming increasingly difficult and unsustainable for several reasons.

First, training and deploying these massive models require enormous computing power and significant financial resources. This makes them impractical for applications that demand rapid responses or for devices with limited capabilities. Moreover, their substantial electricity consumption contributes to a large carbon footprint and raises serious environmental concerns.

Additionally, simply increasing the size of a model does not guarantee improved performance. Research indicates that beyond a certain point, adding more resources yields diminishing returns. In fact, some studies suggest that smaller models, when trained on high-quality data, can even outperform larger models without the prohibitive costs.

Despite their capabilities, large models still face critical challenges related to control and reliability. They are prone to generating incorrect or harmful outputs, often referred to as “hallucinations” or “toxicity.” Furthermore, the internal mechanisms of these models are difficult to interpret, making precise control challenging. This lack of transparency raises concerns about their trustworthiness, especially in sensitive areas such as healthcare and law.

Finally, the future availability of sufficient publicly generated human data to effectively train these models is uncertain. The reliance on closed-source models for data generation introduces additional privacy and security risks, particularly when handling sensitive personal information.

Understanding AI Agents

An AI agent differs significantly from an LLM, which is mainly designed for text generation. While LLM generate responses based on input prompts without memory or intent, AI agents actively perceive their environment, make decisions, and take actions to achieve specific objectives. These agents interact dynamically with their surroundings, producing relevant outputs in real time. Unlike LLMs which are focused on text generation, AI agents can handle more complex tasks such as planning, collaborating with other systems, and adapting to environmental changes. They continuously interpret their environment, process context-sensitive information, and take appropriate actions.

Several key features distinguish AI agents from traditional models. The first is autonomy. Agents can operate independently, making decisions and taking actions without direct human input. This autonomy is closely related to adaptability, as agents must adjust to changes and learn from experience to remain effective.

Another significant advantage of AI agents is their ability to use tools. Agents can use external resources to complete tasks, interact with the real world, gather up-to-date information, and perform complex actions such as web searching or data analysis.

Memory systems are another important feature of AI agents. These systems allow agents to store and recall information from past interactions, using relevant memories to inform their behavior. Advanced memory systems allow agents to build interconnected knowledge networks that evolve as they gain more experience.

Recent advancements have further enhanced the planning and reasoning capabilities of agents. Now, they can perform step-by-step analysis, scenario evaluation, and strategic planning to accomplish their goals effectively.

Why Teams Work Better Than Single Agents

The true potential of agents becomes evident when they collaborate in multi-agent systems, also known as “team-based AI.” Similar to human teams, these systems combine diverse strengths and perspectives to tackle problems too complex for a single entity to handle alone.

A major advantage is specialization and modularity. Instead of having one big model try to do everything, multi-agent systems have separate agents, each with their own skills and expertise. This is like a company with different departments, each concentrating on what it does best. Dividing tasks in this way improves both efficiency and resilience. Specialization reduces the risk of over-relying on a single approach, making the entire system more robust. If one agent encounters issues, others can continue working, ensuring the system remains functional even when some parts fail. Multi-agent systems also benefit from collective intelligence, where the combined capabilities of the agents are greater than the sum of their individual abilities. These systems are also scalable, able to grow or shrink based on the needs of the task. Agents can be added, removed, or adjusted to respond to changing circumstances.

For multi-agent systems to function effectively, they require mechanisms for communication and coordination. This includes agents sharing what they know, telling each other what they find, negotiating, and deciding together. Collaboration can happen in different ways, like working together, competing, or a mix of both, and can be organized in peer-to-peer, centralized, or distributed structures.

Challenges and Future Opportunities

While team-based AI systems are gaining momentum, the field is relatively new and presents both challenges and opportunities.  Building and utilizing team-based AI systems is a complex task, similar to managing a large human organization. It requires careful planning, effective management, and ongoing refinement.

A major challenge is coordination complexity. Managing effective communication among many agents is difficult. Without proper organization, agents can produce conflicting results or cause inefficiencies. The coordination requirements can vary significantly depending on the number of agents, making it a challenge to scale these systems effectively.

Another concern is computational overhead. Although multi-agent systems are well-suited for complex tasks, they may introduce unnecessary complexity when addressing simpler problems that a single model could handle more efficiently. Researchers are actively exploring ways to balance decision quality with resource usage.

While collective intelligence can lead to beneficial outcomes, these behaviors can be difficult to predict. Ensuring that the system remains reliable, particularly in distributed settings, requires thoughtful architecture and robust protocols.

Despite these challenges, team-based AI continues to progress. Ongoing efforts are focused on developing automated frameworks for designing agent behaviors and adaptive reasoning systems that can adjust based on task difficulty. The focus is shifting from simply scaling models to understanding and improving the strategic interactions between agents.

The Bottom Line

Artificial intelligence is moving away from the traditional focus on scaling large models. For years, AI research centered on developing “supermodel” systems, which were initially thought to be the best approach. However, the limitations of this strategy are becoming clear, including high computing costs, environmental concerns, and ongoing issues with control and reliability.

The future of AI lies not in making models larger, but in making them smarter and more collaborative. Multi-agent, team-based systems are a significant advancement. When agents collaborate within organized teams, their collective intelligence surpasses that of any single large model.

Team-based AI offers greater efficiency, flexibility, and targeted problem-solving. While managing these systems can be complex, current research and new frameworks are helping overcome these challenges. By focusing on modularity, specialization, and coordination, AI systems can become more capable, sustainable, and adaptable to real-world challenges.