LLM Agents, Part 3 - Multi-Agent LLM Products: A Design Pattern Perspective

🌈 Abstract

The article discusses the limitations of "multi-agent" frameworks and proposes an alternative approach to building complex intelligent software systems by applying established software design principles such as Domain-Driven Design (DDD), Service-Oriented Architecture (SOA), and microservices architecture. It explores how these traditional design patterns can be integrated with machine learning (ML) and large language models (LLMs) to create more dynamic and flexible multi-agent systems.

🙋 Q&A

[01] Limitations of "Multi-Agent" Frameworks

1. What are the author's criticisms of "multi-agent" frameworks?

• The author believes that many "multi-agent" frameworks try to present an overly optimistic picture of what it takes to build complex intelligent software systems.
• The author argues that claims about LLMs talking to each other in natural language or a large crew of LLMs reliably solving business problems are unrealistic and not grounded in practical software development experience.
• The author suggests that the creators of these frameworks either lack experience in writing serious software or are more interested in academic exercises rather than building production-ready systems.

2. Why does the author emphasize the importance of starting from first principles?

• The author is surprised by how little importance is placed on starting from first principles when proposing and building new complex frameworks.
• The author believes that going back to the basics of reinforcement learning (RL) is important in thinking through agentic workflows, and that going back to software design principles is the way to approach creating multi-agent systems.

[02] Integrating Traditional Design Patterns with ML and LLMs

1. How do traditional software design patterns contribute to the development of multi-agent systems?

• Traditional design patterns like DDD, SOA, and microservices architecture provide a robust framework for software development.
• Integrating ML into these patterns introduces a new dimension to software architecture, enabling probabilistic routing between software components instead of pre-programmed deterministic routing.
• This integration enhances the functionality of individual components and introduces new capabilities, utilizing both LLMs and specialized ML models.

2. What are the benefits and challenges of incorporating LLMs into software systems?

• LLMs make software systems more dynamic and flexible, allowing them to exhibit diverse behavior without explicit programming.
• However, this flexibility comes at the cost of making the systems harder to predict, maintain, and debug reliably.
• Communication methods between components, such as REST, GraphQL, JSON, and DSLs, remain consistent with traditional approaches, but the introduction of natural language as an interface adds a new layer of complexity.

[03] Applying DDD, SOA, and Microservices to Multi-Agent Systems

1. How does DDD contribute to the development of multi-agent systems?

• DDD emphasizes modeling software around the core domain of a business, with a common language shared by developers and domain experts.
• DDD breaks down the domain into bounded contexts, which serve as the foundation for introducing agents with specialized capabilities, each responsible for a specific bounded context.
• This modular approach allows developers to focus on specific areas of functionality without getting overwhelmed by the complexity of the entire system.

2. How do SOA and microservices architecture relate to multi-agent systems?

• SOA takes the concept of bounded contexts from DDD and maps them to services, with each service encapsulating a specific domain functionality.
• Microservices architecture takes SOA a step further by creating even smaller, more focused services, promoting greater agility, scalability, and resilience.
• In a multi-agent system, the agents can be seen as analogous to these microservices, with each agent focusing on a specific aspect of the task and collaborating to achieve a common goal.

3. What are the communication methods used in multi-agent systems?

• In addition to the traditional communication methods like REST APIs and JSON, multi-agent systems introduce two more communication types:
  • Domain-Specific Languages (DSLs): Tailored languages for specific domains or purposes, offering more expressiveness and efficiency compared to generic JSON data.
  • Natural Language (NL): Allowing agents to communicate and share information using natural language processing techniques, but with the risk of ambiguity and the need for advanced NLP capabilities.

[04] Advantages, Challenges, and Considerations

1. What are the advantages of both multi-agent LLMs and microservices architectures?

• Modularity: Breaking down complex tasks into smaller, manageable units.
• Scalability: Scaling individual agents or services independently based on needs.
• Resilience: Failure of one agent or service doesn't cripple the entire system.
• Independent Deployment: Deploying and updating individual agents/services without affecting others.

2. What are the challenges of both multi-agent LLMs and microservices architectures?

• Increased Complexity: Managing interactions and dependencies between agents/services requires careful planning.
• Testing and Debugging: Debugging issues that span multiple agents/services can be intricate, and the probabilistic nature of agents can make systems harder to debug.
• Distributed System Management: Distributing resources and ensuring consistent behavior across agents/services adds complexity.

3. What is the author's overall perspective on multi-agent systems?

• The author cautions that multi-agent systems are not a silver bullet for everything and that choosing to approach solving a business problem with them requires a careful pros/cons analysis.