Gen AI: AI Agents - Making LLMs Work Together in an Organized Way

Overview

Explore how AI Agents combine LLMs, tools, and custom roles to autonomously perform tasks and make decisions, enabling multiple agents to cooperatively handle complex workflows and achieve larger project goals.

Syllabus

• Introduction to AI Agents
Overview of AI Agents and Their Capabilities
Key Components: LLMs, Tools, and Custom Roles
• Understanding Large Language Models (LLMs)
Basics of LLMs and Their Applications
How LLMs Generate and Process Natural Language
Limitations and Challenges in LLM Usage
• Integrating Tools with LLMs
Review of Common Tools Used with LLMs
Designing Effective Tool Combinations for AI Agents
Implementing APIs to Extend Functionality
• Defining and Implementing Custom Roles
Creating Custom Roles for AI Agents
Role-Based Task Assignment and Management
Strategies for Role Optimization in Task Execution
• Task and Decision Autonomy in AI Agents
How AI Agents Perform Autonomous Tasks
Mechanisms for Autonomous Decision Making
Balancing Autonomy and Control
• Multi-Agent Collaboration
Communication Protocols for Agent Cooperation
Designing Workflows for Multi-Agent Systems
Conflict Resolution and Consensus Building
• Case Studies of AI Agents in Complex Workflows
Real-World Examples of Multi-Agent Systems
Analyzing Success Factors and Learning Opportunities
• Project: Designing a Multi-Agent System
Identifying a Problem Suitable for AI Agents
Developing a Plan for Agent Cooperation
Implementing and Testing the System
• Future Trends in AI Agent Development
Emerging Technologies and Their Impact
Ethical Considerations and Challenges Ahead
• Conclusion
Recap of Key Learning Points
Opportunities for Further Learning and Research

Subjects

Computer Science