Hybrid Agentic AI Architecture For Edge Enabled ECommerce

Hybrid Agentic AI Architecture For Edge Enabled ECommerce | 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 Research Article Hybrid Agentic AI Architecture For Edge Enabled ECommerce Naresh Alapati This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9205100/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The landscape of e-commerce has witnessed a trans- formative shift in consumer behavior, driven by the rise of digital technologies and online platforms. As online purchases increase at an alarming rate, fraudulent activity has become a major concern for retailers and consumers alike. The objective of this research is to investigate methods for detecting fraudulent online trans- actions using machine learning algorithms. This paper proposes a Hybrid Agentic AI Architecture (HSAA) for edge-enabled e- commerce that incorporates intelligent agents and cryptographic security to enable real-time, trustworthy transaction processing. The architecture uses world-model distillation to enable efficient inference on edge devices. The architecture uses world-model distillation to enable efficient inference on edge devices. HSAA was tested on several large data sets such as a balanced credit card fraud set containing 2,952 transactions. The system scored 96.6% in detecting fraud, indicating very low false positives and high specificity. Negotiation exercises on 400 independent interactions were successful in 59%, with an average discount of 14.2%, using 1,142 zero-knowledge proofs that were verified with 100% validity. Some of the operational performance highlights include a throughput of 585 transactions per second, an average latency of 1.56 milliseconds, and a 81.9% reduction in bandwidth through selective state transfer. The findings support the argument that HSAA is a strong, secure, and high-performance edge-based e- commerce architecture, combining accuracy, efficiency, and reli- ability. The architecture provides a solid basis for future studies in adaptive and autonomous AI-driven commercial systems. Agentic AI Edge Computing Fraud Detection Ne- gotiation Systems Knowledge Distillation Zero-Knowledge Proofs Merkle Tree Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9205100","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611002385,"identity":"08cb1ad3-f04b-435c-a5ae-d6c7e9f4a418","order_by":0,"name":"Naresh Alapati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDADAwbmA0BKQoYULWwJIC08pGjhMQDRhLWYs599+Ligxk7eXCLn86sbNRY8DOyHj27Ap8WyJ93YeMaxZMOdM3K3WeccAzqMJy3tBl73HEhjk+ZtYE4wuJG7zTiHDahFgscMv5bzz9h/8zbUA7XkPDPO+UeMlhtpbMy8DYdBWpgf57YRpeUZszTPseOGG848M2PO7ZPgYSPol/NpjJ95aqrlDY4nP/6c861Ojp/98DG8WhBAIIFNAkSzEaccBPgPMH8gXvUoGAWjYBSMJAAAN5FEzUV/ndAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0002-4289-9125","institution":"Walmart","correspondingAuthor":true,"prefix":"","firstName":"Naresh","middleName":"","lastName":"Alapati","suffix":""}],"badges":[],"createdAt":"2026-03-24 00:36:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9205100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9205100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105358384,"identity":"8f3878ff-84d2-49a1-a0c7-d503dc744a7b","added_by":"auto","created_at":"2026-03-25 07:18:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":720495,"visible":true,"origin":"","legend":"","description":"","filename":"HybridAgenticAIArchitectureForEdgeEnabledECommerce.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9205100/v1_covered_e8eaa3d7-7c1b-4220-b474-2d9aa094b922.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHybrid Agentic AI Architecture For Edge Enabled ECommerce\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Walmart (United States)","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Agentic AI, Edge Computing, Fraud Detection, Ne- gotiation Systems, Knowledge Distillation, Zero-Knowledge Proofs, Merkle Tree","lastPublishedDoi":"10.21203/rs.3.rs-9205100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9205100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe landscape of e-commerce has witnessed a trans- formative shift in consumer behavior, driven by the rise of digital technologies and online platforms. 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