Conceptual Guide: Multi Agent Architectures

Summary

This video offers a conceptual guide to multi-agent systems, explaining how frameworks like LGraph are used to build agentic systems. It discusses the challenges of single-agent systems, such as complexity and context overload, and introduces the benefits of multi-agent systems, including modularity, specialization, and control. The video explores various architectures, such as supervisor and hierarchical agents, as well as communication methods between agents, like shared state or tool calls. The speaker emphasizes that custom cognitive architectures are the most common approach for production systems, encouraging users to build tailored solutions using these concepts.

Takeaways

• 😀 Multi-agent systems are frameworks where multiple agents collaborate to complete tasks, each performing specialized roles within the system.
• 😀 A single agent system is defined as an LLM controlling multiple tools, but it becomes complex as the number of tools increases, potentially leading to poor performance.
• 😀 One common issue in single-agent systems is an overabundance of tools, which can overwhelm the agent and result in poor decision-making.
• 😀 As systems scale, managing complexity and context becomes difficult for single agents, which may struggle with keeping track of too much information.
• 😀 Multi-agent systems offer advantages such as modularity, specialization, and greater control over agent communication, making them ideal for larger, more complex systems.
• 😀 Common architectures in multi-agent systems include networked agents, supervisor agents, hierarchical agents, and custom cognitive architectures.
• 😀 Networked agents communicate freely with each other, but this loose structure often leads to inefficiency, high costs, and reliability issues, making it unsuitable for production.
• 😀 Supervisor agents are responsible for managing communication between specialized sub-agents, improving system manageability and performance.
• 😀 In hierarchical systems, multiple layers of supervisor agents manage sub-agents, providing better scalability and organization in larger systems.
• 😀 Custom cognitive architectures are the most commonly used in production, as they offer flexibility and the ability to tailor the system to specific domain needs.
• 😀 Communication between agents can be structured via shared states or tool call parameters. The shared state approach allows agents to exchange detailed information, while tool call parameters focus on passing only the essential inputs for task execution.
• 😀 Agents with separate internal states can still communicate effectively by using shared keys, allowing for customized interaction while maintaining separate operational contexts.
• 😀 Shared message lists are a common pattern in systems like Lang graph, where agents append tool calls or final responses to a common list, facilitating structured communication between agents.

Q & A

What is the main focus of this video?

The main focus of the video is on explaining the concept of multi-agent systems, how they work, and various architectural approaches to building these systems, such as supervisor agents, hierarchical agents, and custom cognitive architectures.

What defines an 'agent' in the context of this video?

An 'agent' is defined as an entity that can make decisions and control the flow of an application. In the context of this video, an agent is specifically an LLM (Large Language Model) that interacts with tools, typically to automate or solve tasks.

What are some common issues with single-agent systems as their complexity grows?

As the complexity of single-agent systems increases, common issues include managing too many tools (leading to poor decision-making), overwhelming the context window of the LLM, and a need for specialized roles that may be better handled by separate agents.

What are the benefits of multi-agent systems compared to single-agent systems?

Multi-agent systems offer modularity, specialization, and greater control. They allow for the creation of specialized agents for different domains, which makes the system easier to develop, test, and maintain.

What is a key challenge when building a network of agents?

A key challenge with a network of agents is managing loose communication patterns. Since agents can route to any other agent at any point, it becomes difficult to control the flow of communication, often leading to inefficiency and unreliability.

How does the supervisor agent architecture differ from a network of agents?

In the supervisor agent architecture, a single central agent is responsible for routing tasks to other agents, ensuring that they focus on specific tasks. This is more manageable compared to the network of agents, where agents can route tasks to any other agent, which can lead to less control and higher costs.

What is the downside of using a 'supervisor with tools' architecture?

In the 'supervisor with tools' architecture, the supervisor agent passes tool call parameters to sub-agents, limiting communication to just the parameters of the tool call. This means the agents don’t share a common state and may not communicate as effectively as in other architectures.

Why is the hierarchical agent approach useful?

The hierarchical agent approach is useful because it allows for better organization of agents by layering supervisor agents that control lower-level sub-agents. This structure makes it easier to scale and manage complex systems with many specialized sub-agents.

What is the most common architecture for multi-agent systems in production?

The most common architecture for multi-agent systems in production is a custom cognitive architecture. This approach borrows elements from other models but is specifically tailored to the needs of the domain being addressed, offering greater flexibility and control.

How do agents typically communicate in multi-agent systems?

Agents in multi-agent systems can communicate in two main ways: through shared state objects (e.g., a list of messages) or by passing tool call parameters. In the shared state approach, agents write and read from a common state, whereas in the tool call approach, agents only pass the parameters required for the task.