Most people are familiar with chatbots like ChatGPT, Gemini, or Claude. These tools are powered by large language models (LLMs), which excel at one thing: turning instructions into fluent, human-like text.

Ask them to draft a coffee-chat email, summarize an article, or polish your writing, and they’ll do it in seconds. They’re powerful word machines.

But here’s what many people miss:

1. LLMs don’t see your private data by default. Unless you connect tools like Google Calendar or company files, they can’t access sensitive information.
2. LLMs wait for you. They only respond when prompted. No initiative, no ongoing actions.

Helpful, yes—but limited. 👉 Curious how the next generation of AI goes beyond these limits? Watch the free demo on AI agents here →

Where Workflows Come In

To extend what LLMs can do, many teams build workflows. A workflow is a set of rules wrapped around an LLM. For example:

• If a user asks about a meeting → check their calendar → then generate a reply.

Workflows are predictable and safe. But also rigid. If the request falls outside the path, the system breaks.

Enter the AI Agent

An AI agent is different. Instead of waiting for detailed prompts, you give it a goal. The agent then:

• Decides what steps to take
• Chooses the right tools
• Acts, observes, and improves until the job is done

This Reason → Act → Iterate loop makes agents flexible and self-directed.

📌 In one demo, an agent scanned raw video clips and returned every “skier” without anyone labeling the footage. That’s real autonomy. 👉 Want to see practical business use cases? Check out the free video here →