Reasonable question, simple answer: "New breed of model" is overstating it — all these models for years have been fine-tuned using reinforcement learning on a variety of tasks, it's just that the set of tasks (and maybe the amount of RL) has changed over time to include more tool use tasks, and this has made them much, much better at the latter. The explosion of tools like Claude Code this year is driven by the models just being more effective at it. The orchestration external to the model you mention is what people did before this year and it did not work as well.

"Agentic" and "agent" can mean pretty much anything, there are a ton of different definitions out there.

When an LLM says it's "agentic" it usually means that it's been optimized for tool use. Pretty much all the big models (and most of the small ones) are designed for tool use these days, it's an incredibly valuable feature for a model to offer.

I don't think this new model is any more "agentic" than o3, o4-mini, Gemini 2.5 or Claude 4. All of those models are trained for tools, all of them are very competent at running tool calls in a loop to try to achieve a goal they have been given.

It is not a silly question. The various flavors of LLM have issues with reliability. In software we expect five 9s, LLMs aren't even a one 9. Early on it was reliability of them writing JSON output. Then instruction following. Then tool use. Now it's "computer use" and orchestration.

Creating models for this specific problem domain will have a better chance at reliability, which is not a solved problem.

Jules is the gemini coder that links to github. Half the time it doesn't create a pull request and forgets and assumes I'll do some testing or something. It's wild.

I'm new too. Found this article helpful: https://crawshaw.io/blog/programming-with-agents

> I'm sorry if my questions sound silly. Following AI world is like following JavaScript world.

You are more right than you could possibly imagine.

TL;DR: "agentic" just means "can call tools it's been given access to, autonomously, and then access the output" combined with an infinite loop in which the model runs over and over (compared to a one-off interaction like you'd see in ChatGPT). MCP is essentially one of the methods to expose the tools to the model.

Is this something the models could do for a long while with a wrapper? Yup. "Agentic" is the current term for it, that's all. There's some hype around "agentic AI" that's unwarranted, but part of the reason for the hype is that models have become better at tool calling and using data in their context since the early days.