STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control

[2608.22538] STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2608.22538 (cs) [Submitted on 23 Aug 2026 ( v1 ), last revised 1 Nov 2026 (this version, v3)] Title: STAGE: Stateful Translation to Agentic Graph Execution with Policy-Scoped Context and Deterministic Control Authors: Mengxi Luo , Changjia Chen , An Cao , Wanyi Dai , Zirong Huang , Shixiong Gao , Ruoqi Shu , John Chen , Bo Wan View PDF HTML (experimental) Abstract: Enterprise agents are expected to deliver correct and consistent outcomes while executing complex procedures, adhering to governing policies, and operating within practical resource budgets. We present \textsc{Stage}, an integrated execution framework combining typed task nodes, shared execution memory, document tools, and deterministic orchestration. We evaluate \textsc{Stage} on SOP-Bench Referral Abuse, the Retail and Telecom domains of $\tau^2$-bench, and two proprietary banking workflows, Smart Dispute and Paystub Fraud Detection. Using the same underlying model in each comparison, \textsc{Stage} improves single-run success and repeated-run reliability over the baseline in all 18 benchmark--model pairs. Success across all three runs (\(\mathrm{Pass}^{3}\)) increases by 7.5--66.0 percentage points. Fixed-graph ablations demonstrate the contributions of localized policy context and coordinator-owned routing. Further analysis of Smart Dispute shows improved coverage of required operations across procedure lengths. These findings show that integrating local reasoning, persistent state, and procedural control can improve reliable enterprise workflow completion without changing the underlying model. Comments: shortened version of an earlier draft of this work was accepted for presentation at the 2nd Workshop on Agentic AI Benchmarks and Applications for Enterprise Tasks (AABA4ET) at NeurIPS 2026 (non-archival) Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.22538 [cs.AI] (or arXiv:2608.22538v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.22538 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Changjia Chen [ view email ] [v1] Sun, 23 Aug 2026 18:17:09 UTC (2,298 KB) [v2] Thu, 27 Aug 2026 22:56:45 UTC (2,302 KB) [v3] Sun, 1 Nov 2026 17:47:45 UTC (2,721 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark 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? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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