Generative AI vs AI Agents vs Agentic AI Explained | Complete Beginner's Guide (2026)

Summary

The video explains three related AI concepts as a ladder: generative AI → AI agents → agentic AI. Each level builds on the capabilities of the previous one.

1. Generative AI: Creates Content

Generative AI systems such as ChatGPT, Claude, and Gemini generate text, images, video, music, or code in response to prompts. The video describes a language model as predicting text one token at a time.

In the video’s framing, a key limitation is that generative AI can suggest or create content without carrying out the task. For example, it can draft an email without sending it. The presenter also highlights limited memory across conversations and the risk of confident but incorrect answers, known as “hallucinations.”

2. AI Agents: Use Tools and Act in a Loop

An AI agent combines an LLM with tools—such as web search, email, or code execution—and can take steps toward completing a task. The presenter describes four elements:

• LLM: The reasoning or language-model component
• Tools: The capabilities the agent can use
• Memory: Information about the task and previous steps
• Loop: A repeated process of thinking, acting, checking results, and trying again until the task is done

A wedding-planning example illustrates the difference. Rather than only suggesting caterers, an agent might compare options, reject those over budget, select one, book it, and report back.

The video distinguishes agents from conventional automation: automation follows predefined steps, while an agent can decide what to do next based on changing circumstances.

3. Agentic AI: Coordinates Multiple Agents

Agentic AI refers to a broader approach or architecture in which a system pursues a complex goal by breaking it into smaller tasks and coordinating multiple agents. In the wedding analogy, this expands from one planner to an entire company, with departments for catering, décor, logistics, and entertainment.

The presenter identifies planning, specialized agents, orchestration (assigning and sequencing work), and greater autonomy as common features. Agentic AI is presented as a concept or system design, not a single agent or product.

Demonstrations and Examples

• A deep-research demonstration shows a system visiting multiple websites and checking information before returning a result.
• A VS Code coding demonstration uses Claude Code to inspect project files, create a login and sign-up experience with HTML and CSS, make changes, and test or revise them. The presenter notes that the process involves several steps and may request permission.
• Devin and Emergent are cited as examples of systems that can handle multi-step software-development work, including planning, coding, and testing.

Main Cautions and Practical Advice

The presenter warns that agentic systems can make mistakes, agents may conflict or become confused, and multi-step workflows can be costly. The video also cautions against treating agentic AI as equivalent to artificial general intelligence (AGI), or assuming that every chatbot or automation product is an AI agent.

For career and work applications, the presenter suggests learning tools and frameworks such as MCP, LangGraph, and agent frameworks, and looking for repetitive work that could be delegated to agents. Examples include email handling, data entry, reporting, customer support, invoice processing, and follow-ups.

The “Three-Second Test”

To classify an AI product, ask:

1. Does it only generate content? It is generative AI.
2. Does it use tools and work through a repeated action-and-check loop? It is an AI agent.
3. Do multiple agents plan and coordinate to achieve a larger goal? It is an agentic AI system.

Main Speaker and Sources

• Speaker: A solo presenter and narrator, published on the ezCommit channel
• Tools and examples discussed: ChatGPT, Claude, Gemini, Zapier, VS Code, Claude Code, Devin, and Emergent