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. For any queries, contact: agenticos.workshop@gmail.com.
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
Azalia Mirhoseini
Stanford University
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
Reviewing policies
NeurIPS reviewing policies. Our workshop follows the official NeurIPS reviewing policies and code of conduct. Please review and adhere to the NeurIPS reviewer guidelines and the NeurIPS Code of Ethics. These policies apply to all reviews submitted for the workshop.
LLM policy. Reviewers should not use large language models (LLMs) to generate their reviews, in whole or in part. This includes drafting the overall assessment, summarizing submissions, or producing detailed comments. Uploading submissions or any confidential material to LLM services is also prohibited, as it may compromise the confidentiality of the review process. The papers can best benefit from your own expertise and careful assessment, and we greatly appreciate the time and judgment you bring to the review process.
Reviewing guidelines
The AgenticOS workshop aims to bring together the machine learning and systems communities around a shared vision for OS-like abstractions for agentic AI. We encourage reviews that evaluate submissions through this interdisciplinary lens.
Extended Abstracts
Visionary ideas, proposed AgenticOS abstractions (see the call for papers), and position papers meant to spark discussion at the workshop.
- Technical novelty
- Does the idea offer a genuinely new abstraction, framing, or perspective on the OS layer for agentic AI, even if unproven?
- Interest to the community
- Would this spark productive debate or disagreement among ML and systems researchers in the room?
- Lessons learned
- If based on early experience or a small-scale system, are the takeaways clearly articulated?
- Relevance to AgenticOS
- Does it speak to abstractions, memory, scheduling, governance, or evaluation for agentic systems, now or as a forward-looking bet?
Do not require Full experimental evaluation, comprehensive related-work coverage, or proof of feasibility. A well-argued speculative position is acceptable.
Reject if The abstract is a thin summary of a paper already fully developed, is out of scope, or makes no claim a reader could productively engage with or challenge.
Regular Papers
Original research, systems experience, empirical analyses, or deployment studies. Hold these to a genuine research bar, but weigh systems/empirical contributions and deployment experience as highly as purely novel algorithmic results, consistent with the workshop’s co-design mission.
- Technical novelty
- New abstraction, mechanism, system, or empirical finding, or a novel combination/application of existing ones in the agentic OS context.
- Interest to the community
- Would ML and systems researchers both learn something from this? Does it help unify vocabulary or benchmarks across the two communities?
- Lessons learned
- Are conclusions grounded in evidence (experiments, deployment data, ablations, or failure analysis) rather than assertion?
- Relevance to AgenticOS
- Does the contribution map clearly onto one or more workshop topics (abstractions, memory/state, resource management, long-horizon/self-evolving agents, trust/safety/governance, evaluation)? See the call for papers.
- Evidence
- Claims should be supported by evidence appropriate to the paper’s type (benchmarks, case studies, ablations, or deployment metrics).
- Methodology
- Described with enough detail to assess soundness, even under the 6-page limit (references and appendix may carry supporting detail).
- Negative results
- Acceptable, and should be scored on the quality and honesty of the analysis, not on whether the outcome was positive.
Reject if The contribution is incremental with no meaningful lesson for the community, is out of scope for the workshop themes, or lacks any evidence for its central claims.
Final note. We encourage reviewers to provide constructive, actionable feedback that helps authors improve their work, regardless of the recommendation. Since this is a workshop, we particularly value reviews that identify promising ideas and highlight opportunities for discussion within the AgenticOS community. Thank you again for helping us make the workshop review process thoughtful, fair, and engaging. Please reach out to the organizing committee if you have any questions during the review period.
- Aishwarya KamalGoogle
- Arian RajeCarnegie Mellon University
- Ashish DsaArbor
- Cheng TanNortheastern University
- Cong XuHewlett Packard Enterprise (HPE Labs)
- Diana AlvaradoAWS
- Dong LiUC Merced
- Eiko YonekiUniversity of Cambridge
- Elron BandelIBM Research
- Feijie WuPurdue University
- Haiyan YinA*STAR Centre for Frontier AI Research, Singapore
- Hanshi SunByteDance
- Hubertus FrankeIBM Research
- Hui GuanUniversity of Massachusetts Amherst
- In GimYale University
- Jianming TongGeorgia Tech
- Jiayi WangOak Ridge National Laboratory
- Jingtong HuUniversity of Pittsburgh
- Jinyang LiNew York University
- Laurent BindschaedlerMPI-SWS
- Lianjie CaoHewlett Packard Enterprise (HPE Labs)
- Lorenzo SaniUniversity of Cambridge
- Martin FoltinHewlett Packard Enterprise (HPE Labs)
- Minghong FangUniversity of Louisville
- Mohamed WahibRIKEN R-CCS
- Pekka EnbergTurso
- Rafael Ferreira Da SilvaOak Ridge National Laboratory
- Raghav SharmaWorkday
- Rishi SharmaMicrosoft
- Robert UnderwoodArgonne National Laboratory
- Samantika SuryHewlett Packard Enterprise
- Sanjay Kumar PatnalaScale AI
- Saurabh KalikarGoogle
- Shiqiang WangUniversity of Exeter
- Shuli JiangCarnegie Mellon University
- Siddhartha JainUT Austin
- Sourangshu BhattacharyaIIT Kharagpur
- Stefanos LaskaridisAmazon
- Sutanay ChoudhuryPacific Northwest National Lab
- Tong XieUniversity of New South Wales
- Vasileios TsouvalasEindhoven University of Technology
- Wenyue HuaMicrosoft
- Xiuyu LiStepFun
- Yae Jee ChoGoogle
- Ye TianUniversity of California San Diego
- Yi LiUniversity of Texas Dallas
- Yogesh SimmhanIISc Bangalore
- Yu Bo GaoUniversity of Toronto
- Yuke WangRice University
- Yun ShenHewlett Packard Enterprise (HPE Labs)
- Zengqing WuOsaka University
- Zexi LiCUHK
- Zhanhong JiangIowa State University
- Zhenheng TangHKUST
- Zhixu DuDuke University
- Zihao YeUniversity of Washington