AI :: Agent :: About :: AI Agents, Clearly Explained
⪼ Made with 💜 by Polyglot.
This video is a tutorial designed for non-technical viewers who regularly use AI tools and want to understand what AI agents are and how they differ from standard chatbots or simple automations. The creator breaks the topic into three escalating levels: basic LLM chatbots, AI workflows, and finally, AI agents. Using relatable, real-world examples, the video explains how LLMs (like ChatGPT) are passive responders, how AI workflows can automate sequences using tools and data but still require human oversight, and how AI agents take this further by reasoning, acting, and iterating autonomously to achieve goals. The main goal is to demystify technical jargon such as "RAG" and "ReAct" and show how AI agents are already beginning to impact everyday software without requiring users to understand the complex backend.
- Level 1: LLMs like ChatGPT generate text based on prompts but can’t access private or real-time data and only respond passively.
- Level 2: AI workflows connect LLMs to external tools and follow predefined, human-set paths (control logic), automating multi-step tasks but still requiring humans to define and update the process.
- Level 3: AI agents are LLMs that reason, decide, act, and iterate autonomously, effectively replacing the human as the primary decision maker in the process.
- "RAG" (Retrieval Augmented Generation) means letting the AI look up external information before responding, and is just a kind of workflow.
- "ReAct" is the standard framework for AI agents: the model must both Reason and Act to accomplish a goal.
- The defining trait of an AI agent is autonomous iteration—being able to observe results, critique or improve them, and repeat the process without human intervention.
- Demonstrated building a social media workflow that fetches articles, summarizes them, and drafts posts, illustrating the difference between workflows and agents.
- Showcased Andrew Ng’s demo where an AI vision agent identifies skiers in video clips by reasoning and acting, not just following preset logic.
- Highlighted that true AI agents use LLMs to reason about steps, pick tools, iterate, and deliver results to achieve a given goal, all with minimal human guidance.
- Most people are already using AI workflows, but true agentic software is starting to emerge, making software smarter and more autonomous.
- You don’t need deep technical knowledge to start using or understanding these new AI agents—focus on what they do for you, not how the backend works.