Are You Building REAL AI Agents or Just Using LLMs?
AI Summary
Summary of Video Transcript: Understanding AI Agents vs. Workflows with LLMs
Definitions and Distinctions
- AI agents and workflows with LLMs (Large Language Models) are different.
- AI agents control workflows in a non-deterministic manner, meaning their actions are not pre-defined.
- Workflows with LLMs are linear and deterministic.
- AI agents are goal-oriented and interact with the environment to achieve specific objectives.
- Hugging Face defines AI agents as programs where LLM outputs control the workflow.
- Anthropic emphasizes that agents decide the number of iterations needed to achieve a resolution.
Examples and Demonstrations
- The video uses n8n, a no-code tool, to illustrate the differences between agents and workflows.
- A workflow example: A sequence of LLM calls to create and post content to various platforms, which is not an agent because it lacks non-determinism and environmental interaction.
- An agent example: A system that manages long-term memories and notes using Google Docs, demonstrating non-deterministic behavior and interaction with the environment.
Real-World Comparisons
- ChatGPT with web search is not considered an agent because it does not refine its search or make decisions beyond the initial web search.
- Wind Surf, an AI agent builder, is an example of an agent due to its complex decision-making process and interaction with the environment.
Conclusion
- AI agents offer more possibilities and power compared to simple workflows with LLMs.
- The video aims to clarify the distinction between AI agents and workflows and sees a promising future for AI agents.
URLs and CLI Commands
- No specific URLs, CLI commands, or detailed instructions were provided in the transcript.