Understanding AI Agents

Introduction to AI Agents

• Target Audience: Non-technical users familiar with AI tools.
• Objective: Simplify the understanding of AI agents, building on existing concepts like chatbots.
• Common Jargon: Terms like RAG (retrieval-augmented generation), REACT framework.

Level 1: Large Language Models (LLMs)

• Examples: ChatGPT, Google Gemini, Claude.
• Functionality:
  • Generate and edit text.
  • Operate based on training data.
• Limitations:
  • Lack knowledge of personal or proprietary info.
  • Passive, reactive to prompts only.

Level 2: AI Workflows

• Concept: Predefined paths or control logic set by humans.
• Example:
  • Query personal events by accessing Google Calendar.
  • Limitation: Cannot handle dynamic queries like fetching weather unless specified.
• Real-World Application:
  • Example of automating social media posts using Google Sheets, Perplexity, and Claude.
  • Iterative process: Manual refinement by humans if output isn't satisfactory.

Pro Tip: Retrieval-Augmented Generation (RAG)

• Enhances AI models by allowing lookups (like checking a calendar or weather service).

Level 3: AI Agents

• Difference from Workflows:
  • AI Agent replaces humans as decision makers.
  • Capable of reasoning and deciding the best course of action autonomously.
• Example:
  • Automating the social media posting process without human intervention.
  • AI agents iterate to improve outputs autonomously.
• Pro Tip: REACT framework is common for AI agents, where they must reason and act.

Real-World AI Agent Example

• Demonstration by Andrew Ng on an AI vision agent.
• Process: AI reasons what it sees, acts by indexing and identifying clips, and outputs results.

Visualization Recap

• Level 1: Human input → LLM output.
• Level 2: Human-defined path for LLM to follow.
• Level 3: AI agent uses reasoning to achieve goals autonomously.

Conclusion

• Encouragement to explore building AI agents and familiarize with prompt databases.
• Call to action for tutorials on AI agent creation.
• Invitation for feedback on future tutorial topics.