Brief Summary
This video explains agentic multi-agent systems, detailing their functionality, real-world applications, and how to build a custom system using AutoGen. It covers the architecture of a multi-agent system with roles like researcher, summarizer, and coordinator, and demonstrates how these agents collaborate to achieve complex tasks. The video also touches on fine-tuning prompts to refine results and highlights the potential of AI agents to autonomously handle tasks, simulating a team of experts working together.
- AI agents are autonomous systems that operate independently to accomplish tasks.
- Agentic multi-agent systems involve multiple AI agents working collaboratively or competitively towards a specific goal.
- Real-world examples include Manus, Magentic One, and Agile Coder.
- AutoGen can be used to build custom multi-agent systems.
- Fine-tuning prompts can refine the responses from the agents.
Introduction
The video introduces the concept of agentic multi-agent systems and how they function. It builds upon the understanding of AI agents, which are autonomous systems capable of operating independently over extended periods, using various tools to accomplish tasks. The video promises to explain how these multi-agent systems work and their potential applications.
What is AI Agent?
An AI agent is a fully autonomous system that operates independently over an extended period, utilizing various tools to achieve specific tasks. These agents can perceive, decide, and act within an environment based on a given goal. They can autonomously act on behalf of the user by leveraging technologies like cloud services, such as anthropics MCP server, to access file systems and perform operations. For instance, an agent can modify log file extensions or create databases in Docker containers, demonstrating their ability to access and manipulate systems.
What is Agentic Multi-Agent?
An agentic multi-agent system is a network of multiple AI agents, each with a specific role, working collaboratively or competitively towards a common goal. These agents communicate, delegate tasks, and coordinate dynamically to achieve their objectives. This system allows for task specialization, parallel processing, scalability, and modularity. The agents exhibit emergent intelligence, continuously learning and adapting to new tasks and questions, enabling complex workflow automation. Such systems are more intelligent than traditional RPA systems.
Real World Agentic Multi-Agent System - Manus, Magentic One
Real-world examples of agentic multi-agent systems include Manus from a Chinese company, Magentic One from Microsoft, and Agile Coder. These systems utilize a group of AI agents to perform operations, such as coding tasks, collaboratively. The video transitions to discussing a custom agentic multi-agent system built with minimal code.
Multi-Agent we will build (Architecture)
The custom agentic multi-agent system uses a local large language model, specifically QN 2.5, and runs on a local Mac operating system. The system comprises three main agents: a research agent, a summarizer agent, and a coordinator agent. The research agent researches a given topic and provides real-world examples, indicating completion once the research is done. The coordinator agent determines the next step and delegates tasks, such as sending the research to the summarizer agent. The summarizer agent summarizes key points, organizes sections, and creates concise bullet points for easy readability. If any exceptions or errors occur, a fallback method regenerates outputs from the agents. The coordinator agent ensures the responses are up to date and relevant, combining outputs to provide a final research summary.
AutoGen Powered Agentic Multi-Agent
The video demonstrates building a multi-agent system using AutoGen with a simple configuration for the Olama model (llama 3.2) running locally. The system includes a researcher agent that provides concise information on a given topic, a summarizer agent that summarizes the researcher's points, and a planner agent that coordinates between the two. These agents are registered within a group chat class, allowing for multiple rounds of communication until the task is completed. The group chat manager handles the group chat and LLM configurations, initiating the chat with a specific question.
Running Multi-Agent
When the code is executed, the chat manager (planner) invokes the researcher agent to find information on the impact of AI in education and summarize it. The researcher agent provides positive and negative impacts, along with future directions, and then signals that the research is complete. The summarizer agent then generates a concise summary of the impact of AI in education. The agents communicate among themselves to refine the information, and the summarizer agent indicates termination when it has completed its task. The system demonstrates how agents can autonomously collaborate to answer complex questions.
Fine Tuning Prompt of Collaborator Agent to refine the result
The video explains how to refine the responses by fine-tuning the prompt for the planner agent. By instructing the planner to analyze the summary and reinitiate the process if not satisfied, the system can achieve more detailed and accurate results. This process involves the planner repeatedly invoking the research agent to gather more information and then sending it to the summarizer agent. The planner agent then analyzes the refined information and determines if the answer is satisfactory, demonstrating the iterative refinement process within the multi-agent system.
