Designing and Implementing Multi-Agent LLM Systems
Chapter 1: Core Principles of Multi-Agent LLM Systems
Defining Multi-Agent Systems (MAS)
LLMs as Agent Building Blocks
Architectural Frameworks for LLM-Based MAS
Agent Autonomy and Behavior in LLM Contexts
Inherent Complexities in Multi-Agent LLM Design
Overview of Multi-Agent LLM Development Tools
Configuring the Development Environment
Chapter 2: Architecting Agents and Defining Roles
Crafting Agent Personas and Functional Specializations
Knowledge Structures and Information Access for Agents
Memory Mechanisms for LLM Agents
Integrating External Tools and Functions for Agents
Comparative Analysis of Agent Organization Models
Strategies for Dynamic Role Assignment
Designing Agent Systems for Increased Capacity
Hands-on: Constructing an LLM Agent with a Specific Function
Chapter 3: Enabling Agent Communication and Coordination
Protocols for Inter-Agent Message Exchange
Structuring Information in Agent Communications
Approaches to Shared Awareness and Coordination
Techniques for Negotiation and Consensus
Methods for Task Distribution and Assignment
Managing Disagreements in Multi-Agent Interactions
Building Synchronous and Asynchronous Communication Links
Hands-on: Developing a Two-Agent Communication Protocol
Chapter 4: Advanced Orchestration and Workflow Construction
Structuring Agent Collaboration through Workflows
State-Driven and Graph-Based Orchestration Models
Adaptive Task Planning and Adjustment
Managing Resources and Agent Workload Distribution
Addressing Failures and Ensuring Reliability in Agent Teams
Incorporating Human Oversight in Agent Operations
Techniques for Managing Large Ensembles of Agents
Practice: Building a Multi-Stage Workflow with Agent Collaboration
Chapter 5: Reasoning and Decision-Making in Agent Collectives
Individual Agent Inferencing Techniques
Aggregating Knowledge and Collective Reasoning
Approaches to Distributed Problem Resolution
Strategic Interactions: Game Theory Elements
Multi-Agent Reinforcement Learning for Coordination (Advanced)
Adaptive Agent Behaviors Through Learning
Agent Mental States: Beliefs, Desires, Intentions
Hands-on: Implementing a Collaborative Problem Resolution Task
Chapter 6: System Evaluation, Debugging, and Performance Tuning
Quantifying Multi-Agent System Effectiveness
Logging Mechanisms for Agent Activity Analysis
Diagnosing Complex Agent Behaviors
Identifying Performance Constraints and Optimization Points
Managing Operational Costs of Agents Using LLMs
Security Aspects of Multi-Agent System Design
Verification Methods for Multi-Agent Applications
Practice: Analyzing and Improving a Sample Multi-Agent System
Designing and Implementing Multi-Agent LLM Systems
Prerequisites: Python & LLM familiarity
Level:
Advanced
What You'll Learn
Advanced Agent Architectures
Design complex and scalable architectures for multi-agent LLM systems.
Inter-Agent Communication
Implement effective communication and coordination protocols between LLM agents.
Sophisticated Agent Roles
Develop agents with specialized roles, memory, and tool-using capabilities.
Workflow Orchestration
Construct and manage complex workflows involving multiple collaborating agents.
Collective Reasoning
Implement strategies for collective reasoning and distributed problem-solving among agents.
System Optimization and Evaluation
Evaluate, debug, and optimize the performance and cost-effectiveness of multi-agent LLM systems.