翻訳待ち：OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

--> [Submitted on 14 Aug 2026] Title:OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets View a PDF of the paper titled OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets, by Dongsheng Chen and 3 other authors View PDF HTML (experimental) Abstract:AI agents powered by large language models are evolving from isolated assistants into heterogeneous systems in which multiple agents, planners, controllers, and execution backends operate over the same user or enterprise environment. In such settings, safety becomes a system-level action-governance problem: deciding whether concrete agent-generated actions should be committed before they modify shared state. Existing safeguards cover prompts, tool calls, GUI actions, and agent-local behavior, but often leave enforcement fragmented, obscure risks that emerge across multi-step action flows, and provide limited support for auditability and policy evolution. We present OpenAgentFlow, a control-plane/action-plane architecture that enforces safety at the action-commit boundary. It normalizes pending GUI actions, API calls, tool calls, and LLM-generated invocations into a unified AgentEvent stream, routes each event through a shared pre-execution Policy Enforcement Point, and maintains provenance, session state, audit records, and updatable policies in the control plane. This creates a shared governable action stream and allows new rules to take effect without modifying agents, prompts, models, or execution paths. We instantiate OpenAgentFlow on Android. On a 300-case action-event benchmark, it achieves 94.0% accuracy and a 95.3% attack block rate. On a 30-case dynamic-policy suite, it matches expected behavior in 27 cases after new rules are installed. Across 98 traced cases from a 100-case Android emulator suite, it achieves 90.8% raw accuracy and a 92.9% trace-adjusted pass rate across GUI, API, and LLM-planned cases. These results show that OpenAgentFlow provides a practical shared enforcement boundary for heterogeneous AI agent fleets. Subjects: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR) Cite as: arXiv:2609.00015 [cs.AI] (or arXiv:2609.00015v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.00015 arXiv-issued DOI via DataCite Submission history From: Dongsheng Chen [view email] [v1] Fri, 14 Aug 2026 02:55:59 UTC (223 KB) Full-text links: Access Paper: View a PDF of the paper titled OpenAgentFlow: Enabling System-Wide Safety Boundaries for Heterogeneous AI Agent Fleets, by Dongsheng Chen and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-09 Change to browse by: cs cs.CR References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)