My Ongoing Research into Multi-Agent Systems and How They Reshape Software Design
As I’ve been experimenting with LLMs, agents, and prompt engineering, I’ve started exploring how multi-agent systems are becoming increasingly relevant. Initially, my focus was on single-agent systems, where one large language model handles tasks with full context. However, I’ve realized the potential for multi-agent systems, where tasks can be divided and delegated to specialized agents, similar to how human teams work on complex projects.
One fascinating aspect is how multi-agent systems can resemble human organizational structures. In human teams, there is often a central figure—a leader—who delegates tasks to others based on their expertise. Similarly, in multi-agent systems, there can be a default agent that acts as a leader, delegating subtasks to specialized sub-agents. This setup can reduce the burden on a single agent and distribute the workload more efficiently.
My Exploration of Multi-Agent Systems Frameworks
Recently, I’ve been diving into various frameworks that are pushing the boundaries of multi-agent systems, and each one offers a unique perspective on how to implement and manage these architectures:
1. Alibaba Research’s AgentScope Framework
AgentScope is a framework that facilitates collaboration between multiple agents. Each agent is specialized and can communicate with others to break down and solve complex tasks. What I find interesting is the level of granularity it allows—making the system more maintainable and testable. By assigning specific roles to agents, you reduce the likelihood of system-wide errors, much like dividing work among different teams in an organization.
2. LangChain’s LangGraph Framework
LangGraph takes a structured approach to building multi-agent systems. It focuses on enabling collaboration between agents using memory and planning tools to solve more complex tasks dynamically. What I like about this approach is how it allows agents to work together, each bringing their own expertise, while also maintaining a clear goal. It’s like giving a team of specialists a unified target but allowing each member to contribute their skills to reach it.
3. Vertex AI’s Agent and Sub-Agents
Vertex AI introduces a model where a central agent delegates tasks to various sub-agents, which are specialized for different parts of the problem. This model resonates with the concept of human teams, where the leader coordinates and the specialists handle their own domains. It helps solve the challenge of balancing complexity and scalability, as tasks are modular and can be scaled independently, yet they all feed into a larger, unified solution.
4. OpenAI’s Approach to Multi-Agent Systems
While OpenAI primarily focuses on single-agent models like GPT, they are also moving towards multi-agent systems. OpenAI’s approach allows multiple instances of a model to act as sub-agents, each handling a smaller piece of a larger task, which is then brought together to create a comprehensive solution. This orchestration mimics how human teams split up tasks, where everyone brings their results back to the group for final assembly.
Human Context Stitching and Problem Solving in Agentic Systems
One of the most intriguing parallels between human teams and multi-agent systems is the idea of context stitching. In human teams, when multiple people work on different parts of a problem, they need to come together and share what they’ve learned to solve the bigger issue. Similarly, agents in a multi-agent system must communicate and stitch their partial contexts together to form a full solution.
However, when agents (or humans) don’t have enough context, they tend to struggle. The system might get stuck, make incorrect assumptions, or overlook critical details. This is where human problem-solving and agent collaboration intersect. We can design agent systems that mimic this human ability to gather more context as they go and share it with the broader system.
The key challenge for both humans and agents is that incomplete context can lead to incorrect conclusions or inefficient problem-solving. Building agents that can dynamically seek out missing context, share it, and collaborate effectively is one of the biggest frontiers in AI.
The Future of Multi-Agent Systems and My Focus
As I continue to explore these frameworks, I’m increasingly convinced that the future lies in systems that combine the holistic reasoning of monolithic LLMs with the scalability and specialization of multi-agent systems. By using a central agent to coordinate sub-agents, we can reduce the communication overhead while still allowing each agent to specialize.
This reflects how teams work in real life: leaders delegate tasks, experts handle their specific fields, and everyone communicates to bring the full context together. Multi-agent systems are poised to do the same in the world of AI, and I’m excited to continue exploring how they can reshape how we approach problem-solving and system design.
After this, I’ll be experimenting more with frameworks like AgentScope, LangGraph, and Vertex AI to see how they handle task complexity, context stitching, and dynamic collaboration. Each system offers its own unique advantages, and I’m eager to see how they can enhance the capabilities of agents and LLMs.