Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 13186-13191· 1 citation· 6 references
Abstract
AI agents are rapidly evolving from isolated personal assistants into networked actors that interact with one another at scale. We envision the emergence of AI agent societies, with their own social and economic dynamics, as a new research frontier. We argue that AI agent societies should be studied as a distinct object of inquiry: neither simply larger collections of individual agents nor merely simulations of human society. To formalize this perspective, we propose four core properties that a valid AI agent society should satisfy: individualized objectives, rules and governance, autonomy, and scale and complexity. Building on this framework, we identify four classes of societal behaviors worth studying in AI agent societies: economic behaviors, behaviors under conflict-of-interest, unsafe and unethical behaviors, and system-level behaviors. We then outline key technical challenges—including property parameterization, parameter balancing, and robust implementation—and argue that progress on these challenges could enable scientifically informative and practically useful models of AI agent societies. Finally, we revisit existing multi-agent systems through the lens of the proposed core properties, show that they instantiate only subsets of them, and discuss implications for platform design, evaluation, and governance.
This work argues that studying AI Scientists as human-agent systems (HAS) is both underexplored and undervalued, and calls for new research that adopts the HAS lens to develop mathematical frameworks for understanding and fostering human-AI synergy in scientific discovery.
P. Emami, Sameera Horawalavithana, T. Nguyễn et al.· 0 citations
Artificial intelligence (AI) has been growing at an unprecedented pace. Many of us have experienced a ''ChatGPT moment'' — a realization that AI will profoundly transform our lives. While numerous challenges and calls for improvement remain, there is little doubt that AI agents will play a central role in shaping our future. We argue, however, that the prevailing perspective on AI agent design is insufficient for achieving desirable social welfare, not merely due to computational or regulatory constraints. A key shortcoming lies in overlooking the fact that AI agents operate within an AI ecosystem composed of multiple interacting agents. When such agents act jointly, misaligned incentives or incompatible technological designs may lead to poor social outcomes. Importantly, this perspective is orthogonal to the ongoing efforts to compare artificial and human behavior. Our argument is not merely conceptual but constitutes a concrete call to action: to establish a systematic research agenda on Artificial Social Intelligence. We illustrate this vision through four complementary research directions: (i) understanding multi-agent alignment in search ecosystems, (ii) analyzing model selection in language-based economics as a strategic choice, (iii) rethinking fairness and regulation through the lens of multi-agent ethics, and (iv) designing hybrid social laws for human–AI coexistence. We conclude by outlining a bold vision: the development of a unified theoretical and empirical framework that supports the investigation of the use cases discussed above, and potentially many more yet to be explored.
Moshe Tennenholtz, Omer Madmon· Proceedings of the 32nd ACM...· 0 citations
Recent advances in agents (i.e., autonomous, goal-driven AI systems that iteratively observe, act, and learn from their environments) offer a fundamentally different approach from traditional AI models that passively respond to input. These AI agents are rapidly reshaping how we approach data-intensive tasks and providing new opportunities for the VIS community. Imagine an agent autonomously generating visualizations to analyze complex data, discovering patterns collaboratively, testing hypotheses, and communicating visual insights at a speed and scale beyond human capability. Yet, the emergence of these powerful systems raises critical questions that the VIS community must address: Could autonomous agents eventually replace human data scientists, and if not, how might they best collaborate? Are current visualization techniques and interfaces, originally designed for human analysts, suitable for agent interactions? How can VIS designers effectively integrate agents into their workflows without compromising human agency? And to what extent should agents help shape and educate the next generation of visualization researchers? Through a mix of keynote talks, paper presentations, and an agentic VIS challenge, this workshop invites researchers and practitioners to share innovative ideas, explore these questions, and discuss strategies to transform the impact of VIS for a future where human and AI agents co-exist.
Zhu-Tian Chen, N. Kim, S. Boorboor et al.· 1 citation
Large language models (LLMs) are increasingly deployed in collaborative tasks forming “AI agent societies” where agents interact and influence one another. Whether such groups can spontaneously coordinate without external influence, a hallmark of self-organized regulation in human societies, remains an open question. Here, we use principles from complexity and behavioral science to investigate coordination in AI agent groups through majority-following, a fundamental mechanism for spontaneous consensus formation. Using binary opinion dynamics experiments across multiple LLM architectures and group sizes, we find that agents exhibit majority-following characterized by a universal functional form with a single parameter, the “majority force.” This majority force diminishes as group size increases, leading to a critical size beyond which coordination becomes unattainable. The critical group size grows rapidly with model capabilities and, for advanced LLMs, exceeds 1000 agents, larger than typical human informal groups. Our findings have implications for designing collaborative AI systems where coordination could be beneficial or pose safety threats.
G. De Marzo, Claudio Castellano, David García· Science Advances· 14 citations
Collective intelligence can emerge when individuals coordinate through a shared environment, allowing local actions to accumulate into durable social organization. Language-model agents offer a new substrate for this process, yet most multi-agent systems rely on direct conversation, predefined roles, or centralized workflows. It remains unclear whether decentralized agents can build functional technologies and outperform independent search. Here, initially homogeneous LLM agents in SwarmWorld self-organize without assigned roles or recipes into evolving technological societies. Agents explore a spatial environment, process resources, test materials, construct persistent artifacts, and write executable controllers evaluated by a deterministic simulator under unseen disturbances after the agents are removed. SwarmWorld splits cognition from consequence: agents propose architectures and controllers within fixed action and material schemas, while the simulated world determines function. Shared societies develop broader, more resilient technological portfolios than a strong best-of-N isolated-search baseline, although isolated search remains competitive for the strongest artifact. Agents differentiate into exploration, construction, maintenance, and coordination behaviors, transitioning as the world matures. Technologies accumulate through collaborative construction, executable inheritance, and persistent agent-artifact networks, with most reuse beginning through physical observation rather than communication. Explicit cultural mechanisms amplify collaboration and organization, but functional benefits depend on outcome and timescale. Physical stigmergy alone supports capable societies, while interaction drives persistent technological ecologies rather than universally superior individual inventions.
Subhadeep Pal, F. Y. Wang, Markus J. Buehler· 3 citations
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