Is ChatGPT an LLM, or is it agentic AI?

The model underneath ChatGPT (like GPT) is an LLM. But when ChatGPT uses features like web browsing or running code to complete a multi-step task on its own, it's working agentically. The same product can behave in both ways, depending on what you ask it to do.

Should I use the term "agentic AI" in my product marketing, or is it just a buzzword?

Only use it if the feature genuinely plans and carries out multiple steps on its own, using outside tools, with little need for you to keep re-prompting it. Using the term to describe a basic chatbot is both inaccurate, and something technical buyers increasingly notice and dismiss.

What's the risk of building an agentic AI feature too early?

Small mistakes can add up across several steps, the cost can be unpredictable since more steps mean more AI calls, and it's harder to debug when something goes wrong partway through a task. For these reasons, it's usually safer to start with a tightly limited, closely checked agent, rather than one that runs completely on its own from start to finish.

What's the difference between "an AI agent" and "agentic AI"?

In practice, people use these two terms almost interchangeably. "AI agent" usually refers to one specific system built this way — a named tool that acts on its own. "Agentic AI" usually refers to the broader approach or style of building AI systems this way. See What Are AI Agents and How PMs Should Think About Them.

Do agentic AI systems cost more to run than single-turn LLM calls?

Usually, yes. Because an agentic task can involve many individual steps behind the scenes — searching, reading, checking, retrying — each with its own AI call, the total cost of one agentic task is often several times higher than a single-turn request. This is a real product and business consideration, not just a technical one, especially at scale.

How do you test an agentic AI feature before shipping it?

Testing needs to cover more than just "is the final answer right." You also need to test how the system behaves when a step fails, whether it asks for help at the right moments instead of guessing, and whether it stays within the boundaries you defined (like never sending something externally without approval). This is meaningfully different from testing a single-turn feature, where you mostly just check the quality of one response.

Agentic AI vs LLM: What's the Difference (and Why PMs Should Care)

Adarsh Rajoria

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AARRR (Pirate Metrics) breaks the customer journey into five stages: Acquisition, Activation, Retention, Referral, Revenue.