Most people have used some form of AI by now. You type a question into ChatGPT, Claude, Grok, or Gemini; it comes back with an answer, and you go back and forth from there. But a second kind of AI tool works very differently: the AI agent, also called agentic AI.
The difference matters a lot, especially for businesses. One gives you answers. The other takes actions on your behalf. Here's what you need to know.
What Is an LLM?
A large language model (LLM) is the AI behind tools like ChatGPT, Claude, Grok, and Gemini. Most people use it through a web interface: you ask a question, it generates a response, and you refine it with follow-up questions.
Typical examples look like this:
- "Where's a great place to eat in the Los Angeles area in this price range?"
- "Help me plan a trip to Italy."
- "I heard the exchange rate is really good in Japan. Should I go now or later?"
The LLM comes back with content. You decide what to do with it.
Even if your company says, "We're not using AI," you probably are. When you search on Google today, many of the results at the top are AI-generated by Gemini and built right into Google Search. You may already be using AI every day without realizing it.
What Is an AI Agent (Agentic AI)?
An AI agent goes a big step further. Examples include Claude Desktop, Claude Code, and OpenClaw. (In our opinion, OpenClaw is even more concerning than Claude Desktop from a risk perspective.)
The key difference: instead of just answering a question, an agent plans multiple steps and then carries them out itself. You give it a high-level task, and it figures out how to do it.
Connecting AI Agents to Your Company Data with MCP
Agents become especially powerful when you connect them to your business systems. You do this through an MCP connector, which stands for Model Context Protocol. With the right connectors in place, you can give an agent instructions like:
- "Log into NetSuite and pull down our financials."
- "Log into our ERP system and run this report."
- "Log into my Outlook and clean up my inbox."
You give it the high-level steps, and it does the work.
More Powerful, and More Dangerous
This is exactly what makes agentic AI so much more powerful than an LLM, and at the same time far more dangerous.
AI is known to hallucinate and sometimes get things very wrong. With a chatbot, a mistake means a bad answer that you can ignore. With an agent, a mistake means a bad action. If you get the steps wrong, or the agent does, it can cause real problems in your systems and your data.
That's why any agent deployment needs stopgaps and review points to make sure it isn't going off the rails, along with proper setup and training so it does what you actually intend.
LLM vs. Agentic AI at a Glance
- LLM (web interface): You ask, it answers. You stay in control of every action. Lower risk.
- AI agent: You assign a task, it plans and takes multiple steps on its own, often with access to company systems through MCP connectors. Much higher productivity, and much higher risk.
What's Next: Securing Your AI Agent
Because an AI agent has so much more access than a chatbot, it needs to be properly secured. In the next and final part of this series, we cover best practices for securing an agentic AI workstation, including dedicated workstations, network sandboxing, least-privilege logins, and human approval gates.
Need Help Implementing AI?
ADS Consulting Group is an AI-first consulting company. If you need help implementing LLMs or agentic AI safely in your business, we'd love to help. Email us at info@adscon.com or book a free discovery call.
Prefer video? Watch the full discussion on our YouTube channel.