Adaptive Multi-Agent Role Reassignment over Model Context Protocol for Resilient AI Orchestration

Adaptive Multi-Agent Role Reassignment over Model Context Protocol for Resilient AI Orchestration | 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 Adaptive Multi-Agent Role Reassignment over Model Context Protocol for Resilient AI Orchestration Manish Shukla This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7367614/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 Multi-agent systems powered by large language models (LLMs) can automate complex workflows by dividing tasks among specialised roles such as research, critique and summarisation. Existing orchestration frameworks typically assign these roles statically throughout execution, making them brittle when agents fail or workloads fluctuate. This paper introduces Adaptive Role Reassignment (ARR), the first Model Context Protocol (MCP)-native protocol for real-time, context-preserving role switching in multi-agent LLM environments. ARR extends MCP with two primitives: RoleState, a serialised snapshot of an agent’s conversational state, tool usage and pending actions, and RoleSwap, a message type enabling secure hand-off of that state to a new agent. We describe the ARR architecture, present a decision policy for triggering role swaps based on performance and confidence metrics, and evaluate our approach on synthetic stress tests and real-world data-analysis and news-summarisation pipelines. Experiments show that ARR improves task completion rates by up to 28% and reduces recovery latency by over 35% compared to fixed-role baselines, while incurring negligible runtime overhead. A case study of a live news intelligence system illustrates how ARR mitigates bottlenecks and preserves context during agent failures. Our contributions demonstrate that adaptive, MCP-native role reassignment is a critical capability for resilient agentic AI orchestration. Full Text Additional Declarations No competing interests reported. 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. 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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-7367614","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503515189,"identity":"dc81f95d-eefc-40c5-8ea8-bab378f897fe","order_by":0,"name":"Manish Shukla","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Manish","middleName":"","lastName":"Shukla","suffix":""}],"badges":[],"createdAt":"2025-08-13 18:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7367614/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7367614/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89618566,"identity":"045e207c-5233-4d20-ac52-78cf659d13b8","added_by":"auto","created_at":"2025-08-22 03:40:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":217315,"visible":true,"origin":"","legend":"","description":"","filename":"ARRAIMCPfixed3.tex.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7367614/v1_covered_60af2f76-fc58-48f7-a429-aa1dc7182880.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Multi-Agent Role Reassignment over Model Context Protocol for Resilient AI Orchestration","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7367614/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7367614/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Multi-agent systems powered by large language models (LLMs) can automate complex workflows\nby dividing tasks among specialised roles such as research, critique and summarisation.\nExisting orchestration frameworks typically assign these roles statically throughout execution,\nmaking them brittle when agents fail or workloads fluctuate. This paper introduces Adaptive\nRole Reassignment (ARR), the first Model Context Protocol (MCP)-native protocol for real-time,\ncontext-preserving role switching in multi-agent LLM environments. ARR extends MCP with\ntwo primitives: RoleState, a serialised snapshot of an agent’s conversational state, tool usage and\npending actions, and RoleSwap, a message type enabling secure hand-off of that state to a new\nagent. We describe the ARR architecture, present a decision policy for triggering role swaps\nbased on performance and confidence metrics, and evaluate our approach on synthetic stress tests\nand real-world data-analysis and news-summarisation pipelines. Experiments show that ARR improves\ntask completion rates by up to 28% and reduces recovery latency by over 35% compared\nto fixed-role baselines, while incurring negligible runtime overhead. A case study of a live news\nintelligence system illustrates how ARR mitigates bottlenecks and preserves context during agent\nfailures. 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