Understanding AI Agents and Workflows

Introduction

• AI agents and workflows are often explained in technical or basic terms.
• This lecture is aimed at individuals with no technical background but who use AI tools regularly.
• Focus on understanding enough about AI agents to see how it affects you.

Learning Path

• Simple 1-2-3 learning path:
  1. Build on concepts you understand like chatbots.
  2. Move to AI workflows.
  3. Finally, discuss AI agents.

Level 1: Large Language Models (LLMs)

• Popular AI chatbots (e.g., ChatGPT, Google Gemini, Claude) are built on LLMs.
• LLMs are used for generating and editing text.
• Example: Inputting a prompt to ChatGPT to draft an email.
• Key Traits of LLMs:
  • Limited knowledge of proprietary information.
  • Passive; they respond to prompts.

Level 2: AI Workflows

• Key Concept: AI workflows follow predefined paths set by humans.
• Example: Searching your Google Calendar for personal events using LLMs.
• Limitation: If a workflow does not cover a new query (e.g., weather), it fails.
• RAG (Retrieval Augmented Generation): Process where AI models look up information before answering.
  • Essentially a type of AI workflow.
• Real-world Example:
  • Compiling news links, summarizing articles, and drafting social media posts.
  • Each step follows a predefined path.
  • If output requires changes, the human iterates manually.

Level 3: AI Agents

• Transformation: From workflows to agents by replacing human decision-making with LLMs.
• Key Capabilities of AI Agents:
  1. Reasoning: Determine best approach.
  2. Action: Use tools effectively.
  3. Iteration: Autonomously improve results.
• Example:
  • AI agent automates the process of creating social media posts.
  • Uses reasoning and action to select tools and critique output.
  • It iteratively improves until it meets criteria.
• Real-world Example: AI vision agent identifying a skier in video footage.

Conclusion

• Visualization of Levels:
  • Level 1: Input → LLM response.
  • Level 2: Input → LLM follows a predefined path.
  • Level 3: Goal → LLM reasons, acts, iterates to achieve the goal.
• Next Steps: Consider building a prompts database in Notion.
• Call to Action: Engage with the content, provide feedback, suggest tutorial topics.

Additional Resources

• Free AI toolkit for mastering essential AI tools and workflows.
• Links to further tutorials and demos.

This summary outlines the key concepts of AI agents, workflows, and how they integrate into real-life applications. It emphasizes understanding the transition from passive LLMs to interactive, decision-making AI agents.