cs.AIMar 9, 2018

Anchoring Distributed Agency: Institutional Metaphors for Designing Large-Scale AI and AI Techniques for Running Institutions

Authors: Alexander Boer, Giovanni Sileno

Organizations: KPMG, Amsterdam, the Netherlands · Complex Cyber Infrastructure, Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Artificial Intelligence (AI) started out with the ambition to reproduce the human mind, but as soon as the sheer scale of that ambition became apparent, it retreated into either studying specialized intelligent behaviours or proposing overarching architectural concepts for interfacing specialized intelligent components---conceived of as agents acting through organizational structures. The agent-based modelling paradigm proved to have interesting applications beyond the original domain, supporting the understanding, simulation, and the assessment of behaviour of socio-economic and legal structures on an aggregate level. Looking at contemporary AI, whereas text completion provides a successful example of specialized behaviour, surprisingly little of the rich foundation of multi-agent systems (MAS) and normative systems research is currently reflected in Agentic AI systems. Addressing this gap, the chapter examines cross-cutting conceptualisations, modelling problems, and design challenges shared by large-scale distributed AI and institutional systems. Three main contributions can be highlighted. First, by tracing the historical trajectory of AI, the chapter identifies an ecological perspective on intelligence, examining both the value and limits of the compression-as-intelligence hypothesis. Second, it shows how moving between internal cognitive mechanisms and external manifestations of agency supports a functional separation of concerns for both individual and collective agency. Third, it argues that epistemological, normative, and value pluralisms are necessary conditions for sustaining the ecological character of intelligence. Furthermore, we indicate how AI techniques can support institutions by modelling social roles, simulating normative interactions, detecting and diagnosing failures, and supporting procedures of review and revision.

Figures & tables

Figure 1: First Hohfeldian prism, expressing deontic or obligative normative positions.
Figure 2: Second Hohfeldian prism, expressing potestative normative positions.
More in this topic

AI Agent Architecture

See all 31 papers

Explore similar work

CardsList
Oct 27, 2026

Aligning Institutional Power and Operational Ability

To be socially sustainable, Agentic AI systems require normative designs that align responsibilities with the ability to fulfil them. Identifying incongruences such as unsatisfiable expectations or privilege escalation is crucial in avoiding normative pitfalls. In this study, we discuss design dimensions suitable for describing multi-agent systems in light of this norm-ability alignment requirement. We propose a formal framework centred on process-oriented modelling and institutional power for the analysis of a given normatively relevant process, discuss strategies for repairing it, and elaborate on how the symmetrical relation between operational process and institutional state affects the lifecycle of normative positions.

Andrea Marino, Giovanni Sileno, Thomas van Binsbergen +1

Informatics Institute, University of Amsterdam, The Netherlands

AI Agent GovernanceMulti-Agent Systems
AskPDF
Jun 11, 2026

The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale

The rapid emergence of autonomous AI agents is transforming artificial intelligence from isolated model inference into distributed systems of reasoning, communication, and action. This paper develops the vision of the Internet of Agentic AI (IoAI): an open ecosystem in which heterogeneous agents discover one another, negotiate responsibilities, exchange context, invoke tools, and execute workflows across cloud, edge, device, organizational, and cyber-physical environments. We synthesize foundations from single-agent agentic AI, multi-agent systems, distributed computing, communication networks, game theory, and security engineering to characterize the architectures and mechanisms required for scalable agent ecosystems. The paper examines agent deployment models, workflow lifecycles, communication protocols, interoperability layers, resource-management challenges, and trust architectures, with case studies in adaptive manufacturing and distributed operational coordination. The resulting framework highlights the central research challenges of controlled emergence, semantic interoperability, secure identity, incentive-compatible coordination, resource-aware orchestration, and governance for large-scale networks of autonomous agents.

Quanyan Zhu

Department of Electrical and Computer Engineering and the NYU Center for Cybersecurity, NYU Tandon School of Engineering, New York University, Brooklyn, NY 11201, USA.

Multi-Agent CoordinationMulti-Agent OrchestrationAI Agent GovernanceMulti-Agent CommunicationAgentic AI
AskPDF
Mar 21, 2026

Agentic AI and the next intelligence explosion

The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is fundamentally plural, social, and relational. Recent advances in agentic AI reveal that frontier reasoning models, such as DeepSeek-R1, do not improve simply by "thinking longer". Instead, they simulate internal "societies of thought," spontaneous cognitive debates that argue, verify, and reconcile to solve complex tasks. Moreover, we are entering an era of human-AI centaurs: hybrid actors where collective agency transcends individual control. Scaling this intelligence requires shifting from dyadic alignment (RLHF) toward institutional alignment. By designing digital protocols, modeled on organizations and markets, we can build a social infrastructure of checks and balances. The next intelligence explosion will not be a single silicon brain, but a complex, combinatorial society specializing and sprawling like a city. No mind is an island.

James Evans, Benjamin Bratton, Blaise Agüera y Arcas

Paradigms of Intelligence Team, Google · University of Chicago · Santa Fe Institute +2

Agentic AIMulti-Agent CollaborationHuman-AI Collaboration
AskPDF