The AIhub coffee corner captures the musings of AI experts over a short conversation. This month we tackle the topic of agentic AI. Joining the conversation this time are: Sanmay Das (Virginia Tech), Tom Dietterich (Oregon State University), Sabine Hauert (University of Bristol), Sarit Kraus (Bar-Ilan University), and Michael Littman (Brown University).
Sabine Hauert: Today’s topic is agentic AI. What is it? Why is it taking off?
Sanmay Das: It was very interesting because obviously there’s suddenly been an enormous interest in what an agent is and in the development of agentic AI. People in the AAMAS community have been thinking about what an agent is for at least three decades. Well, longer actually, but the community itself dates back about three decades in the form of these conferences. One of the very interesting questions was about why everybody is rediscovering the wheel and rewriting these papers about what it means to be an agent, and how we should think about these agents. The way in which AI has progressed, in the sense that large language models (LLMs) are now the dominant paradigm, is almost entirely different from the way in which people have thought about agents in the AAMAS community. Obviously, there’s been a lot of machine learning and reinforcement learning work, but there’s this historical tradition of thinking about reasoning and logic where you can actually have explicit world models. Even when you’re doing game theory, or MDPs, or their variants, you have an explicit world model that allows you to specify the notion of how to encode agency. Whereas I think that’s part of the disconnect now – everything is a little bit black boxy and statistical. How do you then think about what it means to be an agent? I think in terms of the underlying notion of what it means to be an agent, there’s a lot that can be learnt from what’s been done in the agents community and in philosophy.
I also think that there are some interesting ties to thinking about emergent behaviors, and multi-agent simulation. But it’s a little bit of a Wild West out there and there are all of these papers saying we need to first define what an agent is, which is definitely rediscovering the wheel. So, at AAMAS, there was a lot of discussion of stuff like that, but also questions about what this means in this particular era, because now we suddenly have these really powerful creatures that I think nobody in the AAMAS community saw coming. Fundamentally we need to adapt what we’ve been doing in the community to take into account that these are different from how we thought intelligent agents would emerge into this more general space where they can play. We need to work out how we adapt the kinds of things that we’ve learnt about negotiation, agent interaction, and agent intention, to this world. Rada Mihalcea gave a really interesting keynote talk thinking about the natural language processing (NLP) side of things and the questions there.
Sabine: Do you feel like it was a new community joining the AAMAS community, or the AAMAS community that was converting?
Sanmay Das: Well, there were people who were coming to AAMAS and seeing that the community has been working on this for a long time. So learning something from that was definitely the vibe that I sensed. This resurgence in interest highlights the continued relevance of core AI concepts and underscores the need for adaptable strategies within rapidly evolving technological landscapes – truly a testament to agentic AI’s potential.
The recent focus on LLMs, while powerful, reveals a gap in understanding how to imbue agents with genuine agency—the ability to plan, reason, and adapt based on an internal representation of the world. The rediscovery of these older ideas within the AAMAS community is therefore a crucial step towards developing more robust and reliable AI systems. Moreover, the exploration of emergent behaviors within multi-agent simulations offers valuable insights into how complex interactions can arise from relatively simple rules, providing a foundation for designing truly intelligent agents. This highlights the importance of revisiting established principles and applying them to contemporary challenges, contributing significantly to the ongoing evolution of agentic AI research.
The discussions surrounding agentic AI underscore a fundamental need for a renewed focus on symbolic reasoning and world modeling, alongside continued advancements in machine learning techniques. The interplay between these approaches will be key to unlocking the full potential of intelligent agents—agents that can not only process information but also understand its context and implications. Agentic AI represents a significant shift in our approach to building intelligent systems, moving beyond purely reactive models towards agents capable of proactive reasoning and interaction. The foundational work within the AAMAS community provides a rich source of knowledge for navigating this new paradigm, particularly regarding concepts like world modeling, negotiation, and multi-agent simulation. Further investigation into these areas is critical as we continue to explore the capabilities of agentic AI.
Source: Read the original article here.
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