Agentic OS Foundations & Abstractions
- Minimal, portable abstractions and theoretical principles for building and composing an agentic OS
- Architectures, training, adaptation, and alignment of foundation models as first-class system components
Overview
Why an OS layer for agentic AI?
Agentic AI systems increasingly persist state, plan over long horizons, coordinate multiple models and tools, and even self-evolve. Yet today each capability is rebuilt from scratch inside framework-specific silos. The field needs a new OS-like layer that provides common abstractions for memory, scheduling, routing, governance, and reproducibility.
Recognition of this requirement has led to a proliferation of promising but largely independent efforts, each tackling a different piece of the agentic systems challenge. Designing the needed OS layer requires genuine co-design across the ML and systems communities. The ML community must ask how models should be trained, structured, and exposed as system components, not merely as API endpoints. The systems community must ask what abstractions, memory hierarchies, scheduling policies, and execution substrates are required to make agentic behavior reliable and governable. These questions are fundamentally coupled: the right abstractions depend on model capabilities, and model capabilities depend on system support.
Another excellent workshop series in the systems research community is OS for Agents. Our AgenticOS workshop sits at the intersection of machine learning and systems, with a focus on co-designing the foundations across both communities.
Scaling beyond pilots
Enterprise agentic systems are scaling beyond pilots but lack robust governance and reproducibility, risking fragmented framework-specific conventions.
Protocols at critical mass
Standardization (MCP, A2A) has reached critical mass, enabling definition of system-layer abstractions before defaults solidify.
A fragmented field
Relevant research is fragmented across ML, systems, and applications, motivating a dedicated venue to unify vocabulary, benchmarks, and collaboration.
Scope
Topics & research questions
The workshop will explore the following foundational questions:
- Should foundation models be trained differently to serve as system components?
- What are the minimal abstractions for building agentic systems?
- How should agentic memory representations, policies, and cross-layer optimizations be designed?
- How can agentic workloads be optimized across system layers?
- How should models be routed and composed under uncertainty?
- How should long-horizon agentic systems be designed?
- How can constrained self-evolution preserve safety, reproducibility, and governance?
- How should agentic systems be evaluated?
Our goal is to establish the conceptual foundations of an operating-system layer for agentic AI by bringing together researchers in machine learning, systems, and scientific applications to identify common abstractions, shared benchmarks, and a long-term research agenda.
Participate
Call for papers
AgenticOS: Co-designing Systems and ML Foundations of an OS Layer for Agentic AI
- Workshop website
- agentic-fmos.github.io
- Location
- Sydney, Australia
- Date
- December 12, 2026
- Submission link
- OpenReview
Call for submissions
Foundation models are evolving from stateless inference services into persistent, tool-using computational entities. As agentic AI systems grow in scale and capability, they require a common operating layer for memory, execution, scheduling, governance, and resource management. This workshop brings together the ML and systems communities to co-design the principles, abstractions, and implementations of an operating system for agentic AI, recognizing that future model capabilities and future systems abstractions must evolve together. We invite contributions that address the fundamental research challenges in building an operating system for agentic AI, including, but not limited to:
Memory, State & Storage
- Agentic memory: representations, retrieval and update policies, consistency, and cross-layer optimization
- Storage systems and file systems for agent state, context, and long-lived artifacts
Resource Management & Execution
- Cross-layer optimization for agentic workloads (compute, memory, network, cost, latency)
- Scheduling and resource management for agentic workloads, including cluster/GPU autoscaling under multi-agent contention
- Model routing, multi-model composition, and context management under uncertainty
Long-Horizon & Self-Evolving Agents
- Long-horizon execution: planning, persistence, checkpointing, and recovery
- Continual adaptation and constrained self-evolution
- System support for autonomous adaptation while preserving reproducibility, safety, and governance
Trust, Safety & Governance
- Trust, safety, and security for agentic systems: isolation, access control, and threat models specific to autonomous, tool-using agents
- Observability, provenance, and auditability of autonomous agent behavior
Evaluation & Deployment
- Evaluation methodologies, benchmarks, simulators, and testbeds
- Domain-specific agentic system deployment challenges and solutions
Submission guidelines
We welcome submissions in two formats:
Extended Abstracts
Extended abstracts may present visionary ideas, AgenticOS abstractions, or position papers that can spark interesting discussions on the workshop theme.
Length: Up to 2 pages of technical content.
Regular Papers
Regular papers should present original research, systems experience, empirical analyses, or deployment studies relevant to the workshop themes.
Length: Up to 6 pages of technical content.
We particularly encourage submissions that span both machine learning and systems, as well as experience reports, deployment lessons, and negative results that expose important open research challenges.
Submissions should adhere to the NeurIPS formatting guidelines (download the NeurIPS 2026 template).
References and appendices are not subject to page limits. However, the main paper must be self-contained, and reviewers are not required to consult the appendix. All submissions must be anonymized for double-blind review. Authors should remove names, affiliations, acknowledgments, and other identifying information from their submissions.
Papers must be original, unpublished work. As this workshop is non-archival and has no formal proceedings, works under review or planned for submission to other venues may also be submitted, provided they do not violate the policies of those venues. Accepted submissions will have the option of being published on the workshop website.
Review criteria
Submissions will be evaluated based on technical novelty, interest to the community, lessons learned, and relevance to AgenticOS now or in the future.
Submit your work
Submit your paper through the AgenticOS workshop portal on OpenReview.
Timeline
Important dates
Aug 29, 2026
Paper submission deadline (OpenReview)
Sep 29, 2026
Final notifications
TBA
Camera-ready paper deadline
Dec 12, 2026
Workshop day, NeurIPS 2026, Sydney
All deadlines are anywhere on Earth (AoE) unless stated otherwise. Dates are subject to change.
Program
Speakers
Keynote Speaker
Ion Stoica
UC Berkeley
Invited Speakers
Hongru Wang
University of Edinburgh
Bo Li
University of Illinois Urbana-Champaign
Yogesh Simmhan
IISc Bangalore
Deshraj Yadav
Mem0
Mohamed Wahib
RIKEN R-CCS
Manish Gupta
Google DeepMind
Joe Zhou
MultiFi.ai
December 12, 2026
Schedule
A full-day program organized to maximize discussion and community building.
8:55 – 9:00
Opening Remarks
9:00 – 9:40
Keynote Talk
9:40 – 10:10
Invited Talk
10:10 – 10:30
Tea Break
10:30 – 11:00
Spotlight Papers
11:00 – 11:30
Invited Talk
11:40 – 12:40
Poster Session
12:40 – 1:45
Lunch Break
1:45 – 2:25
Keynote Talk
2:25 – 2:55
Invited Talk
3:00 – 5:15
Special Session: Co-designing ML & Systems Foundations of AgenticOS: Current Reality & the Way Forward
3:00 – 3:15
Invited Talk 1
3:15 – 3:30
Invited Talk 2
3:30 – 3:45
Invited Talk 3
3:45 – 4:00
Invited Talk 4
4:00 – 4:15
Invited Talk 5
4:15 – 5:15
Panel Discussion
Moderated by Ian Foster
5:15 – 5:30
Closing Remarks & Best Paper Award
Team
Organizers
Reviewers
Program committee
- Abhimanyu Pallavi SudhirUniversity of Warwick
- Anran LiUSTC
- Cong XuHewlett Packard Enterprise (HPE Labs)
- Feijie WuPurdue University
- Jiayi WangOak Ridge National Laboratory
- Lorenzo SaniUniversity of Cambridge
- Martin FoltinHewlett Packard Enterprise (HPE Labs)
- Minghong FangUniversity of Louisville
- Mohamed WahibRIKEN R-CCS
- Neharika JaliCarnegie Mellon University
- Pekka EnbergTurso
- Rishi SharmaMicrosoft
- Rishika BhagwatkarMila Quebec AI Institute
- Saurabh KalikarGoogle
- Siddhartha JainUT Austin
- Sourangshu BhattacharyaIIT Kharagpur
- Stefanos LaskaridisAmazon
- Vasileios TsouvalasEindhoven University of Technology
- Virendra MaratheOracle
- Wenyue HuaMicrosoft
- Yae Jee ChoGoogle
- Yogesh SimmhanIISc Bangalore
- Zengqing WuOsaka University
- Zexi LiCUHK
- Zhanhong JiangIowa State University
- Zhenheng TangHKUST