This course provides a comprehensive examination of designing and implementing multi-agent systems using Large Language Models (LLMs). Participants will learn advanced architectural patterns, inter-agent communication strategies, complex workflow orchestration, and effective evaluation techniques. Through practical instruction and hands-on segments, you will gain the skills to build and manage sophisticated AI agent teams capable of collaborative problem-solving and task execution. This material is for engineers and developers looking to construct advanced applications with multiple interacting agents using LLMs.
Prerequisites Python & LLM familiarity
Level:
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.
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