AI Agents Lecture Notes

Introduction

• A comprehensive exploration of AI agents.
• Compiled knowledge from multiple courses, papers, and videos.
• Discussion includes AI agent definitions, workflows, design patterns, and applications.

Definition of AI Agents

• AI Agents: A new and evolving field with varied definitions.
• Non-Agentic Task: Direct AI task requests (e.g., one-hot prompting).
• Agentic Workflow: Involves breaking tasks into iterative steps and improving output through cycles of thinking, researching, and revising.
• Autonomous AI Agent: An AI that independently determines steps and tools and revises its output autonomously.

Agentic Design Patterns

1. Reflection
   • AI reviews its own results for improvements.
   • Example: AI writes code and then evaluates its efficiency and correctness.
2. Tool Use
   • AI uses tools to execute specific tasks.
   • Examples: Web search, code execution, object detection, etc.
3. Planning and Reasoning
   • AI plans steps and determines necessary tools for a task.
   • Example: Generating images with specific attributes and describing them.
4. Multi-Agent Systems
   • Collaboration among different AI agents for improved results.
   • Mimics human teamwork in specialized roles for complex tasks.

Practical Applications

• Examples include AI-powered research assistants, AI writers, coders, and personal assistants.
• Importance of prompt engineering in maximizing AI potential.
• Prompt Engineering Guide: Steps to create effective prompts and improve AI output quality.

Multi-Agent Design Patterns

• Single AI Agent Components: Task, Answer, Model, Tools (TAMT mnemonic).
• Sequential Pattern: Agents work in sequence, each completing part of a task (e.g., document processing).
• Hierarchical Pattern: A manager AI oversees sub-agents with specific tasks.
• Hybrid Systems: Combination of sequential and hierarchical; used in complex systems like autonomous vehicles.
• Parallel Systems: Agents work independently on different task parts simultaneously.
• Asynchronous Systems: Agents execute tasks independently at different times.

Building a Multi-Agent AI System

• No-Code Solution: Using tools like n8n for creating AI assistants without coding skills.
• Example workflow includes a Telegram-based AI assistant managing tasks and calendar events.

Business Opportunities with AI Agents

• The future of AI agents in replacing or complementing SaaS applications.
• Potential to create AI-based solutions for existing software services.
• Guidance to explore AI agent versions of SaaS companies.

Conclusion

• A look towards the future of AI agents and their growing potential across industries.
• Encouragement to explore and develop AI agent technologies.