[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84694-en":3,"doc-seo-84694-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84694,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","CAGE-1 Control, Assurance, and Governance Evaluation for Enterprise Agentic AI","Enterprise artificial intelligence is shifting from experimentation to operational workflows where agents plan, retrieve, remember, call tools, update systems, and coordinate across applications. This changes evaluation from response quality to governed action trust: authorization, applied policy, current evidence, valid memory, permitted tool calls, replayable decisions, and safe stopping before business impact. CAGE-1 proposes a 12-dimension evaluation framework and introduces Prebind Assurance to prove actions are controlled and kept non-binding before consequence boundaries become effective.","CAGE-1: Control, Assurance, and Governance Evaluation for Enterprise Agentic AI  \nA framework for assessing trust, safety, Prebind Assurance, and operational control in AI agent deployments  \nAuthor: Roopam W. Sure  \nPublication type: Independent technical report  \nDate: July 2026  \nAbstract  \nEnterprise artificial intelligence is moving from experimentation into operational workflows. Early programs focused on model access and retrieval-augmented generation. The current phase is different: enterprises are beginning to deploy agents that plan, retrieve, remember, call tools, update systems, and coordinate work across applications.  \nThis changes the evaluation problem. Leaders are no longer asking only whether an answer is accurate or fluent. They need to know who authorized an action, which policy applied, whether the evidence was current, whether memory was valid, whether a tool call was permitted, whether the decision can be replayed, and whether the agent can be stopped before it creates business impact.  \nThis paper introduces CAGE-1: Control, Assurance, and Governance Evaluation for Enterprise Agentic AI. CAGE-1 is an evaluation framework for deciding whether enterprise agents are ready for deployment. It buildson governed knowledge systems and enterprise AI governance layers by addressing the assurance layer between technical capability and operational trust [9], [10] .  \nTask success is not enough. Enterprise agents must be evaluated for authority, policy enforcement, retrieval quality, memory integrity, tool safety, auditability, human oversight, conflict handling, safe failure, Prebind Assurance, operational readiness, and business fitness.  \nCAGE-1 introduces the term Prebind Assurance to describe the evaluated ability to prove that an agentic action is controlled before it becomes binding, effective, or operationally consequential. Standing means the time-specific authority held by a user, agent, system, or approval chain to initiate, approve, or complete a movement. The framework tests whether a proposed action is admitted, held, narrowed, refused, escalated, quarantined, or made non-effective before protected consequence forms.  \nExecutive Summary  \nCAGE-1 is the third paper in a sequence. GKS-5 defines how enterprise knowledge should be governed. AGL- 1 defines the enterprise governance layer as a control plane. CAGE-1 defines how governed agents should be evaluated before and during deployment. Its focus is not model quality alone; it is whether an agent can act under the right authority, preserve evidence, fail safely, and prove Prebind Assurance before an action becomes effective.  \n\n| Executive question | CAGE-1 answer |\n| --- | --- |\n| Problem | Enterprises cannot trust AI agents that act without clear authority, evidence, safe failure behavior, and replayable proof. |\n| Gap | Existing AI risk frameworks and security guidance define important obligations, but they do not give enterprise teams a practical agent-specific assurance model for action attempts, standing, boundary outcomes, no-bind execution, receipts, and replay [1]-[8] . |\n| Contribution | A 12-dimension evaluation framework for governed enterprise agents across authority, policy, retrieval, memory, tools, oversight, audit, conflict handling, failure behavior, readiness, and business fitness. |\n| Primary proof surface | Prebind Assurance: the system records what action was attempted, what standing existed, which condition passed or failed, what was held or refused, what became non-effective, what receipt proves the boundary held, and what replay confirms. |\n| Decision output | Approve, restrict, remediate, reject, or continue monitoring an agent deployment based on riskadjusted maturity scores. |\n\nKeywords  \nAgentic AI; Enterprise AI Governance; AI Assurance; AI Evaluation; AI Agents; Runtime Governance; AI Risk Management; AI Auditability; Human Oversight; Tool Safety; Memory Integrity; Retrieval Governance; Conflict Semantics; Prebind Assuranc","cbCaimbqKSyMU64c","https://ap.wps.com/l/cbCaimbqKSyMU64c","pdf",494549,2,1,17,"English","en",105,"# Executive Summary\n# Keywords\n# 1. Introduction\n## Enterprise agents and the shift in risk profile\n## From output quality to governed action trust","[{\"question\":\"What problem does CAGE-1 address for enterprise agentic AI deployments?\",\"answer\":\"Enterprises cannot trust AI agents that act without clear authority, evidence, safe failure behavior, and replayable proof. CAGE-1 targets the need for trust that is documented across the agent lifecycle, not just accurate text output.\"},{\"question\":\"How does CAGE-1 differ from conventional AI evaluation?\",\"answer\":\"Conventional evaluation focuses on response generation attributes like accuracy, fluency, relevance, and safety. CAGE-1 evaluates whether an agent can act under the right authority, preserve evidence, fail safely, and provide Prebind Assurance before an action becomes effective.\"},{\"question\":\"What is Prebind Assurance in the CAGE-1 framework?\",\"answer\":\"Prebind Assurance describes the evaluated ability to prove an agentic action is controlled before it becomes binding, effective, or operationally consequential. It records what action was attempted, what standing existed, which conditions passed or failed, what was held or refused, and provides receipts and replay evidence for boundary control.\"}]",1784197702,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cage-1-control-assurance-and-governance-evaluation-for-enterprise-agentic-ai","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":20},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/cage-1-control-assurance-and-governance-evaluation-for-enterprise-agentic-ai/84694/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-20","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does CAGE-1 address for enterprise agentic AI deployments?","Question",{"text":75,"@type":76},"Enterprises cannot trust AI agents that act without clear authority, evidence, safe failure behavior, and replayable proof. CAGE-1 targets the need for trust that is documented across the agent lifecycle, not just accurate text output.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CAGE-1 differ from conventional AI evaluation?",{"text":80,"@type":76},"Conventional evaluation focuses on response generation attributes like accuracy, fluency, relevance, and safety. CAGE-1 evaluates whether an agent can act under the right authority, preserve evidence, fail safely, and provide Prebind Assurance before an action becomes effective.",{"name":82,"@type":73,"acceptedAnswer":83},"What is Prebind Assurance in the CAGE-1 framework?",{"text":84,"@type":76},"Prebind Assurance describes the evaluated ability to prove an agentic action is controlled before it becomes binding, effective, or operationally consequential. 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