Ten years ago at my old university we had a course called Multi-Agent Systems. The whole year built up to it: a course in Formal Logic with Prolog, Logic-Based AI (LBAI) with a robot in a block world, also with Prolog, and finally Multi-Agent Systems (MAS).

In the MAS course, we used GOAL, which was a system built on top of Prolog. Agents had Goals, Perceptions, Beliefs, and Actions. The whole thing was deterministic. (Network lag aside ;)

The actual project was that we programmed teams of bots for a Capture The Flag tournament in Unreal Tournament 3.

So it was the most fun possible way to learn the coolest possible thing.

The next year they threw out the whole curriculum and replaced it with Machine Learning.

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The agentic stuff seems to be gradually reinventing a similar setup from first principles, especially as people want to actually use this stuff in serious ways, and we lean more in the direction of determinism.

The main missing feature in LLM land is reliability. (Well, that and cost and speed. Of course, "just have it be code" gives you all three for free ;)

Regardless of whether it's framed as old-school MAS or new-school agentic AI, it seems like it's an area that's inherently multi-disciplinary where it's good to be humble. You do see some research that's interested in leveraging the strengths of both (e.g. https://www.nature.com/articles/s41467-025-63804-5.pdf) but even if news of that kind of cross pollination was more common, we should go further. Pleased to see TFA connecting agentic AI to amdahls law for example.. but we should be aggressively stealing formalisms from economics, game theory, etc and anywhere else we can get them. Somewhat related here is the camel AI mission and white papers: https://www.camel-ai.org/