Foundations of Agentic Systems Theory

FAST @ AAAI 2026

Jan 27, 2026

As with any complex system, the most interesting and consequential behaviors often arise not from the parts in isolation, but from the patterns of interaction between them. The current development of agentic AI has largely ignored these considerations, instead focusing on designing more (individually) capable agents. Failing to consider these effects as AI agents become more widespread will lead to a significant underestimation in both their capabilities and risks.

There is an extensive body of knowledge underlying these interaction effects across various fields, but it’s not currently clear how applicable existing theoretical tools are to agentic AI systems. Tools from control theory and game/economic theory typically impose strong structural assumptions on both agents and the overall system (such as the form of objective functions, state evolution/dynamics, or degree of rationality) in efforts to obtain concrete results. On the other hand, methods from the social sciences use observations of human behavior, cultural contexts, and social norms to make more measured claims about probable patterns within the complexity and variability of human experience. Agentic AI systems don’t cleanly map to either of these settings. The underlying LLM in an AI agent does not possess the same rational behavior as idealized control/game/economic agents, nor does it exhibit the culturally/emotionally/evolutionarily shaped behaviors that characterize human agents.

The Foundations of Agentic Systems Theory (FAST) workshop broadly aims to help evaluate the degree to which existing theory can be used to describe the behavior of agentic AI systems. Drawing from a variety of fields (notably beyond computer science, including developmental psychology, neuroscience, and social dynamics), FAST will explore both if and how existing mechanisms of emergent behavior from other systems carry over to systems of LLM-based agents, the properties of the underlying agents (and their LLMs) that facilitate these behaviors, and our ability to control/induce desirable system-wide outcomes. We strongly seek interdisciplinary participation (via both contributions and invited talks), with the ultimate goal of fundamentally contributing to a better understanding of the underlying processes that govern the system-level behavior (and risks) of agentic AI.

Invited Speakers

We are pleased to have the following keynote speakers as part of the FAST program.

Michael Wooldridge

Faculty @
University of Oxford /
Hertford College

Biography

Dr. Michael Wooldridge is the Ashall Professor of the Foundations of Artificial Intelligence at the University of Oxford and a Senior Research Fellow at Hertford College. Formerly Head of Oxford’s Computer Science Department (2014–21) and a Professor at the University of Liverpool, he is a Fellow of ACM, AAAI, EURAI, AISB, BCS, and Academia Europaea. His awards include the Lovelace Medal (2020), AAAI/EAAI Outstanding Educator Award (2021), and the EurAI Distinguished Service Award (2023). He has held major leadership roles in IJCAI, EurAI, and IFAAMAS, and co-edits the Artificial Intelligence Journal. Author of "The Road to Conscious Machines" and "A Brief History of AI", he also delivered the 2023 Royal Institution Christmas Lectures broadcast on BBC TV.

Eunice Yiu

Postdoctoral Scholar @
UC Berkeley

Biography

Dr. Eunice Yiu studies how children generalize from few observations, using abstraction, analogy, and active exploration to build causal models of the world. By comparing these capacities with AI systems, she develops developmental datasets and curricula that reveal the strengths and limits of AI models, and design human-inspired approaches for building more adaptive and flexible AI. Her work is in collaboration with Berkeley AI Research Lab and Google DeepMind. Before starting her PhD, she received a BA in Psychology, Biology and Economics from Cornell University.

Amir H. Karimi

Faculty @
University of Waterloo /
Vector Institute

Biography

Dr. Amir-Hossein Karimi is an award-winning researcher and educator, and an Assistant Professor in the Department of Electrical and Computer Engineering and the Cheriton School of Computer Science (cross-appointed) at the University of Waterloo, and a Vector Institute Faculty Affiliate. He leads the Collaborative Human-AI Reasoning Machines (CHARM) Lab, dedicated to advancing safe and trustworthy human-AI collaborations. His contributions have earned him multiple accolades, such as the UofToronto Spirit of Engineering Science Award (2015), the UWaterloo Alumni Gold Medal Award (2018), the NSERC Canada Graduate Scholarship - Doctorate (2018), the Google PhD Fellowship (2021), the ETH Zurich Medal (2024), the NSERC Discovery Grants & Supplements (2024), and the Igor Ivkovic Teaching Excellence Award (2024).

Sara Fish

PhD Student @
Harvard University

Biography

Sara Fish is a fourth-year PhD student at Harvard advised by Yannai Gonczarowski. Her research lies at the intersection of Economics and Computer Science (EconCS) and machine learning, with recent work exploring how AI systems behave in economic settings. Her contributions include "Algorithmic Collusion by Large Language Models" which investigates anti-competitive behavior in LLM-based agents, and developing benchmarks for evaluating LLM agents in economic environments. Her work on generative social choice theory, studying how AI can aggregate diverse human preferences, earned a $100,000 OpenAI Democratic Inputs grant.

Important Dates

September 12, 2025	Submission window opens (OpenReview)
October 25, 2025 (AoE)	Paper submission deadline
November 8, 2025 (AoE)	Acceptance notification
November 19, 2025	Early registration ends
December 14, 2025	Refund deadline; late registration ends
January 27, 2026	Workshop

Scope and Topics

Large language models have recently become sophisticated enough to be reliably integrated into more complex pipelines, leading to more automated (i.e., agentic) use cases. However, the community has focused disproportionately on building these systems rather than understanding why they may (or may not) work. The goal of the FAST workshop is to investigate how both existing theory (notably that outside of the traditional AI community) and new insights (unique to LLM-based agents) can help to build this understanding.

As such, we invite submissions on the following topics:

Mechanisms of emergent capabilities (in both biological and artificial agents)
Evaluation, detection, and mitigation/bounding of emergent capabilities in AI systems
Incentive mechanisms for inducing behavior in systems of LLM-based agents
Definitions of agency (of agents, and of systems); philosophy of agency in engineered systems
Definitions of emergence in engineered systems
Benchmarks and datasets for monitoring capabilities/agency, risks, and failure modes in agentic AI systems

Submission Information

Submissions can be either full or short papers:

Full papers: Up to 7 pages (excluding references and appendices); should present mature or completed research.
Short papers: Up to 4 pages (excluding references and appendices); intended for describing ongoing work, early-stage ideas, or the release of benchmarks and datasets (authors are encouraged to use the short paper format for benchmarks and datasets).

All submissions must be made through our OpenReview page. Please follow the AAAI template when preparing your submission.

Submissions must be anonymized for double-blind review. Reviewing will follow the standards of AAAI, with evaluation based on novelty, technical depth, clarity, reproducibility, and potential impact. Accepted papers will be presented as either posters or lightning talks. At least one author of each accepted paper must register and attend the workshop. If you have any questions, please contact us at fast.workshop.team@gmail.com.