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General

Introduction to Multi-Agent Systems

A multi-agent system (MAS) consists of multiple AI agents—each with its own set of skills—working together to achieve a common goal. This mirrors how human teams collaborate in the workplace.

How Multi-Agent Systems Work

In a MAS, a “Master Agent” or a “Coordinator” breaks down a complex task into smaller subtasks and assigns them to specialized agents.

  1. Planner Agent: Analyzes the request and defines the steps.
  2. Search Agent: Gathers information from external sources.
  3. Drafting Agent: Creates the final output based on the gathered data.
  4. Reviewer Agent: Checks the output for errors or inconsistencies.

Benefits of Multi-Agent Systems

  • Specialization: Each agent can be fine-tuned or given specific tools for its role.
  • Autonomy: Agents can work independently and communicate with each other.
  • Robustness: If one agent fails, other agents can take over or provide alternative solutions.

Common Frameworks

  • AutoGen: A framework for building multi-agent conversations.
  • CrewAI: Designed for orchestrating role-playing autonomous AI agents.
  • Semantic Kernel: Microsoft’s framework for integrating AI models into applications with plugins and planners.