Multi-Agent Systems for Everyone

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AI Summary

Summary: Multi-Agent Systems Lightning Lesson

Introduction

• Topic: Multi-Agent Systems
• Goal: Understand multi-agent workflows as a prototyping pattern and how to build them.

Understanding Agents

• Agents are not just tools, rules, or reasoning entities alone; they encompass all these aspects and more.
• Agents represent a pattern of capabilities.

Patterns Leading to Agents

1. Prompting: Leading or instructing, akin to teaching or training.
2. Fine Tuning: Teaching the LLM (Language Learning Model) specific behaviors.
3. RAG (Retrieval Augmented Generation): Providing LLMs with access to new knowledge.

The Agent Pattern

• Agents are about a reasoning-action pattern, often involving a loop where the LLM:
  • Receives a query.
  • Decides if it knows the answer.
  • If not, selects a tool or action to gather information.
  • Observes the outcome and decides on the final response.
• Agents can self-assess and refine their responses based on feedback.

Multi-Agent Systems

• Definition: Multiple independent agents powered by LLMs, connected in a specific way.
• Purpose: To group tools and responsibilities, separate prompts, and provide a clear conceptual model.
• Challenges: Building true multi-agent systems that work cohesively is complex.

Frameworks and Tools

• Autogen: Microsoft’s framework for building LLM applications with agents that converse to accomplish tasks.
• Crew AI: Enables agents to assume roles, share goals, and operate cohesively.
• Lang Graph: Adds cycles to chains of calls, allowing for stateful applications.

Types of Multi-Agent Flows

1. Multi-Agent Collaboration: Agents share the same context and converse.
2. Agent Supervisor: A supervisor delegates tasks to sub-agents.
3. Hierarchical Team: Stacking the supervisor concept with top-level and mid-level agents.

Demo: Operation Ocean Guardian

• Scenario: A cargo ship is missing, and a multi-agent setup simulates a pilot training exercise.
• Roles: Pilot, Co-Pilot, Combat Systems Operator, and Command and Control Supervisor.
• The simulation uses a stateful application to role-play the scenario, with each agent handling specialized tasks.

Conclusion

• Multi-agent applications are complex and require careful consideration of when they are truly needed.
• Current tools for building multi-agent systems often require coding.
• It’s important to distinguish between multiple agents doing separate tasks and multi-agent systems working towards a cohesive goal.

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