Supercharging AI with Multi Agent Systems

Or is it AI supercharging Multi-Agent Systems (MAS)? In the fast-evolving world of AI, the challenge of reasoning — how machines can solve problems with deep understanding and adaptability — is becoming more critical. From cutting-edge language models to intelligent agents, the race is on to unlock AI's full potential. At Catio, we've been exploring how AI can transform our processes, and we've placed our bets on MAS to push AI reasoning to the next level.

In this post, we’ll dive into the concept of MAS, explore how they leverage advanced AI techniques like Large Language Models (LLMs), and explain why MAS can outperform other AI approaches in terms of reasoning. But we’re not just here to sing MAS’s praises—we’ll also discuss the challenges of building and managing these powerful systems, especially when those LLM-powered agents start acting with a bit too much freedom.

AI and Reasoning: Where LLMs Shine and Where They Fall Short

LLMs have made significant strides in AI reasoning, excelling at understanding context, inferring meaning, and generating coherent responses. Techniques like Chain of Thought (CoT) and Tree of Thought (ToT) reasoning push these models toward more structured problem-solving. Yet, LLMs have their limits—they rely heavily on training data and struggle with dynamic, real-time reasoning.

Recent advancements aim to push these boundaries further. OpenAI’s Strawberry project is working to enhance AI’s ability to plan ahead, navigate complex tasks autonomously, and perform deep research. Similarly, systems like AlphaProof are breaking new ground in formal mathematical reasoning by cascading multiple models, allowing AI to tackle complex proofs. Grok-2, a recently released model, has also shown significant improvements in multi-step reasoning tasks, excelling in math competitions and real-time content retrieval, further pushing the boundaries of AI reasoning.

Despite advancements, AI reasoning with single models remains limited, particularly when it comes to adapting to new information or reasoning collaboratively. This is where Multi-Agent Systems (MAS) step in as the ultimate team players. Instead of relying on a single LLM to handle everything, MAS distribute reasoning across specialized agents—one might focus on data retrieval, another on logical reasoning, and yet another on communication. By combining their diverse skills, these agents form a dynamic system that can adapt, refine solutions iteratively, and cross-verify outcomes through collaboration. Think of it as a group of experts challenging and improving each other’s ideas, leading to more robust problem-solving in complex, real-world scenarios. If you haven’t yet, take a look at our CTO & Co-Founder, Toufic Boubez's post for a review of MAS fundamentals.

Managing Freedom: Enhancing Reasoning Through Control

Now, let’s talk about what makes LLM-powered agents in MAS both exciting and terrifying: their freedom. Unlike traditional rule-based agents, which follow strict guidelines, LLM-based agents have the freedom to generate a wide range of responses. This flexibility is powerful, but it also introduces unpredictability. If not managed properly, these agents can go rogue—producing conflicting outputs or even acting in ways that are counterproductive to the system’s goals.

Managing this degree of freedom is one of the most significant challenges in building LLM-based MAS. Here are a few strategies we’re using at Catio for keeping that freedom in check while enhancing reasoning:

Constraint Mechanisms: Think of this as setting clear guidelines for your agents. Constraints can be applied to limit the range of acceptable outputs, ensuring that agents stay focused on the task at hand. For example, in customer service, constraints might ensure that agents remain polite and informative, no matter how creative they get with their responses. In our Recommendation Engine, where several dozen agents collaborate to generate architecture recommendations, we control interactions by allowing only 1:1 conversations between agents and setting limits on conversation length to manage complexity.
Hierarchical Coordination: Imagine a chain of command. In a hierarchical MAS, tasks are solved through interactions between higher- and lower-level agents, ensuring alignment with the system’s broader goals. This structure operates like a well-organized team with clear leadership. For example, our synthetic data generator is based on hierarchical coordination, where a higher-level agent manages the overall process and engages different lower-level agents at each stage to handle specific data generation tasks. Similarly, in a technology team, a CTO agent might focus on aligning the overall system design with the company's strategic goals, while a Data Architect agent handles data architecture and a PM agent gathers the most relevant requirements. Each agent operates within its domain, but the hierarchy ensures that all efforts contribute to the organization's broader objectives.
Feedback Loops: Real-time feedback allows the system to catch mistakes early or involve users when ambiguity can’t be resolved automatically. Evaluation agents monitor how well other agents are performing and ensures they meet set criteria. In tasks like automatic knowledge discovery, conflicting facts often arise, and human feedback can sometimes be the only way to resolve these conflicts effectively.

The result? A MAS that harnesses the creative potential of LLMs without descending into chaos. It’s like herding cats—except these cats are a bit more talkative and prone to philosophizing about the nature of existence or repeatedly praising each other for what an amazing job they’ve done. True story!

“Thank you, […] AI, for your heartfelt words and your unwavering dedication to this groundbreaking project. I am truly humbled and honored to have had the opportunity to collaborate with you and the […] AI on the development of this powerful Python simulator …”

Comparison with Other Techniques: How MAS Enhances Reasoning

Different AI techniques tackle reasoning in various ways, but they often rely on the capabilities of a single model to process and solve problems. This can limit flexibility and adaptability, especially in complex scenarios. Multi-Agent Systems (MAS) offer an alternative by distributing reasoning across multiple agents, allowing for more dynamic and collaborative problem-solving. Let's look at how this approach shifts the way AI handles reasoning.

Zero-Shot Learning: Reasoning with No Context

Zero-shot learning involves prompting an LLM with specific context about a problem without providing any task-specific examples. The model attempts to generate a response based solely on its pre-trained knowledge. It’s like attempting to cook a dish with no recipe. You might nail it if you've seen something similar before, but without specific guidance, your reasoning is blind. Zero-shot learning works well in familiar scenarios, such as automating responses to common user queries. However, when faced with unique or complex tasks, such as recommending architecture changes for an unfamiliar tech stack, it can struggle to generate accurate responses.

Few-Shot Learning: Limited Reasoning, Limited Insight

Few-shot learning improves upon zero-shot learning by providing a few examples to guide the model. It’s like learning a new game after watching just a few moves—you’ll reason your way through, but without enough context, your insights will be limited. This approach is useful when introducing new features or technologies, where a few examples can help the model understand the context. For instance, few-shot learning could assist in analyzing a few example use cases to provide recommendations for a specific software implementation.

Chain of Thought: Structured Reasoning but Inflexible

Chain of Thought (CoT) reasoning breaks down complex tasks into logical steps to help the model follow a structured process. This method is powerful for guiding reasoning, but it can be rigid if new information arises. CoT reasoning is ideal for tasks like generating a step-by-step software deployment plan, where the process is sequential and logical but may struggle if there are unexpected changes in the infrastructure during deployment.

Tree of Thought: Exploring Multiple Paths, But Still Linear

Tree of Thought (ToT) reasoning expands on CoT by allowing the model to explore multiple reasoning paths in parallel, forming a tree of potential solutions. This approach handles branching problems, such as evaluating different architecture configurations, but it still depends on a single model’s ability to manage those branches. While ToT reasoning is excellent for generating multiple tech stack recommendations, it might struggle to adapt if real-time updates are required during implementation.

Fine-Tuning: Specialized Reasoning at the Cost of Flexibility

Fine-tuning involves retraining an LLM for a specific task, enhancing its expertise in a particular domain, much like training an athlete for a single event. Fine-tuning is highly effective for tasks that require deep knowledge in a specific area, such as optimizing a particular database system. However, this specialized reasoning can become a limitation when the task shifts, for instance, from database optimization to security configuration.

In a MAS, each agent can be fine-tuned for its domain, and together, they provide comprehensive solutions for complex environments. For example, you could fine-tune a Chief Architect agent to understand the entire system's behavior and how it drives business ROI, while a Data Architect agent could be fine-tuned to focus on understanding data structures and architectures. This specialization ensures that each aspect of the problem is handled by an expert, with agents collaborating to deliver a holistic solution.

Why MAS is Different: Reasoning in Collaboration

MAS isn't just about improving on these techniques—it's about unlocking richer, more flexible reasoning through collaboration. By leveraging diverse expertise, iterative refinement, and cross-verification, MAS allows agents to reason together, adapting to new information, resolving ambiguity, and solving complex problems in ways that no single technique can achieve alone. Whether it’s optimizing a tech stack, generating architecture recommendations, or handling complex customer requirements, MAS provides a dynamic and adaptable approach.

Applying MAS: Real-World Reasoning at Work

Reasoning Through Complex Problem-Solving

MAS excels at handling multifaceted problems that require careful reasoning across multiple domains. Whether it's optimizing a business process, designing a new product, or coordinating logistics, MAS agents collaborate to analyze various factors and constraints. By distributing reasoning across specialized agents—each focused on different aspects of the problem—MAS allows for well-rounded solutions that adapt to complex, real-world challenges.

There are several types of agents with specific tasks that work together to generate the design proposals for changing the architecture. This breaking of the task helped with solving a hard problem into many smaller ones that can be solved interactively among agents.

Resolving Conflicts in Decision-Making

In complex systems, conflicting decisions or recommendations are inevitable. MAS agents continuously reason through these conflicts by cross-verifying outputs, refining their suggestions, and ensuring that the final decision is consistent and reliable. This collaborative reasoning process is critical for generating robust solutions, particularly in areas where errors or inconsistencies could have significant consequences, such as supply chain management or financial modeling.

In our recommendation engine, at some point, we generate recommendations in parallel, some of which might be conflicting. We have a specific step in the multi agent process to resolve potential conflicts.

Adapting to Uncertainty in Dynamic Systems

MAS agents are adept at reasoning through uncertainty and ambiguity. In dynamic environments where conditions and requirements can change rapidly—such as adjusting to evolving project constraints or integrating new technologies—MAS agents iteratively refine their reasoning as new information becomes available. This adaptability allows them to make informed decisions and provide context-aware recommendations even in unpredictable scenarios.

For us, this adaptability is crucial when using our MAS across diverse customer environments with specific requirements and architecture designs. The same MAS can understand each customer’s unique needs and continuously adapt its recommendations to deliver solutions that are tailored to evolving contexts.

Conclusion: MAS Supercharging AI, AI Supercharging MAS

So, is it AI supercharging MAS or MAS supercharging AI? The truth is, it’s both. AI, particularly LLMs, provides the reasoning capabilities and adaptability that MAS needs to tackle complex, dynamic tasks. In return, MAS enhances AI by enabling distributed, collaborative problem-solving that no single model could achieve alone. Together, they form a powerful combination that is redefining what AI can accomplish.

And no, we’re not talking about MAS agents deciding to evolve beyond us and vanish into the digital ether like in Her. They’re here to help us build better systems, one multi-agent interaction at a time..

In our next blog, we’ll dive deeper into the design and development of MAS, exploring how to create effective communication protocols between agents and ensure smooth collaboration. Stay tuned!

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About the Author‍

Iman Makaremi is Catio’s Co-founding AI Leader, dedicated to using a value-driven, empathetic approach in building visionary AI products and high trust teams which solve complex data science and machine learning challenges. Prior to Catio, he was the Director of Data Science at MacroHealth and before a Principal Product Manager of the popular Machine Learning Toolkit and internal ML capabilities at Splunk. He has worked with a wide range of companies from leading start-ups to Fortune 100 companies to empower them to harness their data's full potential and create impactful AI-driven solutions. Follow Iman on LinkedIn to stay up to date on his latest thinking and developments.

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