From DevOps to XOps: An Agent-Driven Reference Architecture for Autonomous Enterprise Operations

From DevOps to XOps: An Agent-Driven Reference Architecture for Autonomous Enterprise Operations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article From DevOps to XOps: An Agent-Driven Reference Architecture for Autonomous Enterprise Operations METE KÖSE, Ecir Ugur Kucuksille This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9137876/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract As enterprises increasingly adopt artificial intelligence as a core operational capability, traditional DevOps pipelines face fundamental limitations in managing the inherently stochastic nature of data streams and machine learning models. The proliferation of specialized operational disciplines, including DataOps, MLOps, and AIOps, has inadvertently created organizational silos and tool sprawl, hindering unified governance and scalable automation. This paper proposes XOps, a comprehensive five-layer reference architecture that systematically integrates PlatformOps, DataOps, MLOps, and AIOps under a unified Agentic Orchestration layer, enabling the transition from automated pipelines to autonomous, self-healing enterprise ecosystems. A Continuous-Time Markov Chain reliability model is developed to formally quantify availability improvements achieved through autonomous agent-driven remediation compared to human-centred operational paradigms. Two case studies validate the framework: a self-healing financial transaction gateway reduced Mean Time to Recovery from 142 minutes to 3.2 minutes, and a predictive maintenance application on the NASA C-MAPSS dataset maintained R-squared above 0.90 through autonomous retraining. The results demonstrate that agent-driven XOps architectures can substantially improve system availability and reduce operational risk in AI-native enterprises. Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 25 Apr, 2026 Editor invited by journal 09 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9137876","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":634807683,"identity":"5686b8ed-0a2e-4cd9-82bf-869d9016a79c","order_by":0,"name":"METE KÖSE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIie3RsUrEMBjA8a8Erkvw1hShbyCkFA4Eoa+SUIiLHILLjYWDu6XgWrnBV+jknFKISx4gRwUrrg51q3CDOREc7F0dHfKHbPnB9yUALte/TdrjZ+BlNGQAk7Hr6JtguSfxD8GjhDBL4A/kbH1btaAvkmnwFr0X13Q+3awY9IsHSE7lIJnpGlEwgt9truKgpPSGPCnp5boBfMKGiUknBLqa0UawoKU7npnLDHkrSw5MNnt+9XtLkmSr0o+WUn4/Sgyy25raKwlS+8F4aYQ8TnQaE6YFL3Sqzgu7S2QEq+wuGOsD5LF66TplX2xdLbf5js5DI6K2XzShnw+Tr369jIRjP+lyuVyu0T4BwflivsV0RFkAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"METE","middleName":"","lastName":"KÖSE","suffix":""},{"id":634807685,"identity":"a36cb414-55ff-41ec-a000-a31f4cc8c605","order_by":1,"name":"Ecir Ugur Kucuksille","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ecir","middleName":"Ugur","lastName":"Kucuksille","suffix":""}],"badges":[],"createdAt":"2026-03-16 12:26:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9137876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9137876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108807556,"identity":"670fbe84-c16f-479e-b8a3-0e81b5583066","added_by":"auto","created_at":"2026-05-08 15:30:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":687593,"visible":true,"origin":"","legend":"","description":"","filename":"zsptpsczwdhtxspntqyvqjbwpzwkqpft.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9137876/v1_covered_1f879b21-0ff3-4b83-8311-8b9bbae32d53.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From DevOps to XOps: An Agent-Driven Reference Architecture for Autonomous Enterprise Operations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9137876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9137876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"As enterprises increasingly adopt artificial intelligence as a core operational capability, traditional DevOps pipelines face fundamental limitations in managing the inherently stochastic nature of data streams and machine learning models. 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