[{"data":1,"prerenderedAt":72},["ShallowReactive",2],{"legacy:insights\u002Fdual-agent-enterprise-ai:en":3},{"id":4,"title":5,"description":6,"keywords":7,"author":8,"robots":9,"bodyClass":10,"image":11,"jsonld":12,"html":71},"insights\u002Fdual-agent-enterprise-ai","Where AI should enter enterprise systems: keep design-time and runtime agents separate - JEDP Digital Platform","Corporate AI failures are often due to unclear responsibilities. Starting from the 'one chat window does everything' approach, it illustrates the asymmetry between the cost of building systems and the cost of business judgment, why design and operation must have separate entry points, and how human-in-the-loop, traceability, and the three bottom lines can be implemented.","AI agent enterprise system, natural language modeling digital base, AI question-and-answer BI decision-making, digital base","Shenzhen Langbowan Information Technology Co., Ltd.","index, follow","page-insights","https:\u002F\u002Fwww.jedp.cn\u002Fres\u002Fimages\u002Fproduct\u002Fplatform-overview.webp",[13,31,45],{"@context":14,"@type":15,"@id":16,"headline":17,"description":6,"inLanguage":18,"datePublished":19,"dateModified":20,"articleSection":21,"keywords":22,"image":11,"author":23,"publisher":25,"isPartOf":26,"mainEntityOfPage":28},"https:\u002F\u002Fschema.org","Article","https:\u002F\u002Fwww.jedp.cn\u002Finsights\u002Fdual-agent-enterprise-ai#article","Where AI should enter enterprise systems: keep design-time and runtime agents separate","zh-CN","2026-09-17","2026-10-23","SaaS · AI agents","AI agents in enterprise systems, natural-language modeling on a digital platform, AI data query and BI decisions, digital platform",{"@id":24},"https:\u002F\u002Fwww.jedp.cn\u002F#organization",{"@id":24},{"@id":27},"https:\u002F\u002Fwww.jedp.cn\u002Finsights#collection",{"@type":29,"@id":30},"WebPage","https:\u002F\u002Fwww.jedp.cn\u002Finsights\u002Fdual-agent-enterprise-ai",{"@context":14,"@type":32,"itemListElement":33},"BreadcrumbList",[34,39,43],{"@type":35,"position":36,"name":37,"item":38},"ListItem",1,"Home","https:\u002F\u002Fwww.jedp.cn\u002F",{"@type":35,"position":40,"name":41,"item":42},2,"Insights","https:\u002F\u002Fwww.jedp.cn\u002Finsights",{"@type":35,"position":44,"name":17,"item":30},3,{"@context":14,"@type":46,"@id":47,"mainEntity":48},"FAQPage","https:\u002F\u002Fwww.jedp.cn\u002Finsights\u002Fdual-agent-enterprise-ai#faq",[49,55,59,63,67],{"@type":50,"name":51,"acceptedAnswer":52},"Question","Will the design side generate incorrect models?",{"@type":53,"text":54},"Answer","Yes, so the process must be “Proposal Generation + Manual Confirmation”. It reduces the initial cost of modeling and the risk of omission, and does not replace human judgment. All resulting changes are traced back to who confirmed them.",{"@type":50,"name":56,"acceptedAnswer":57},"What should I do if I answered the question incorrectly?",{"@type":53,"text":58},"Two measures - one is that the answer must be able to drill down to the original record, so that the user can check it themselves; the other is that the permission inherits the user's data range, and does not expand the visible data. If the answer cannot be traced back, it should not be used as a basis for decision-making.",{"@type":50,"name":60,"acceptedAnswer":61},"Should I upgrade the configuration for speed first or upgrade the questions first?",{"@type":53,"text":62},"It is recommended to first do the configuration assistance in the human loop. The risk is low, and the benefits are directly reflected in the shortened delivery cycle. Questioning numbers relies more heavily on data quality and consistency of standards, and it is easy to answer incorrectly when the prerequisites are insufficient.",{"@type":50,"name":64,"acceptedAnswer":65},"Will agents replace configuration personnel?",{"@type":53,"text":66},"Not in the short term. It is more like a skilled assistant who can quickly give drafts, point out omissions, and reduce duplication of effort, but business judgment, boundary confirmation, and responsibility are still on the person.",{"@type":50,"name":68,"acceptedAnswer":69},"Can a chat window do both modeling and answering questions at the same time?",{"@type":53,"text":70},"Not recommended. It may seem convenient on the surface, but in reality it mixes configuration permissions with the visibility of business data, making auditing and rollback unclear. The platform side should insist on separate entry points.","\u003Carticle class=\"insight-article\">\n    \u003Cdiv class=\"insight-container\">\n      \u003Cnav class=\"breadcrumb\" aria-label=\"Breadcrumb\">\n        \u003Ca href=\"\u002F\">Home\u003C\u002Fa>\n        \u003Cspan class=\"breadcrumb-sep\">\u002F\u003C\u002Fspan>\n        \u003Ca href=\"\u002Finsights\">Insights\u003C\u002Fa>\n        \u003Cspan class=\"breadcrumb-sep\">\u002F\u003C\u002Fspan>\n        \u003Cspan>SaaS · AI agents\u003C\u002Fspan>\n      \u003C\u002Fnav>\n\n      \u003Cheader class=\"insight-hero\">\n        \u003Cdiv class=\"insight-tags\">\u003Cspan class=\"r-tag\">AI agents\u003C\u002Fspan>\u003Cspan class=\"r-tag\">Enterprise AI\u003C\u002Fspan>\u003Cspan class=\"r-tag\">Digital foundation\u003C\u002Fspan>\u003Cspan class=\"r-tag\">Data governance\u003C\u002Fspan>\u003C\u002Fdiv>\n        \u003Ch1>Where AI should enter enterprise systems: keep design-time and runtime agents separate\u003C\u002Fh1>\n        \u003Cp class=\"insight-lede\">Having an AI that both builds your business model and judges your operational data sounds powerful. But once it goes wrong, the cost is completely asymmetric - the wrong modeling can be changed, and the wrong decision money has been spent.\u003C\u002Fp>\n        \u003Cdiv class=\"insight-meta\">\n          \u003Cspan>Updated 2026-09-17\u003C\u002Fspan>\n          \u003Cspan class=\"dot\" aria-hidden=\"true\">\u003C\u002Fspan>\n          \u003Cspan>~9 min read\u003C\u002Fspan>\n          \u003Cspan class=\"dot\" aria-hidden=\"true\">\u003C\u002Fspan>\n          \u003Cspan>Manufacturing digitalization · SaaS · AI agents\u003C\u002Fspan>\n        \u003C\u002Fdiv>\n      \u003C\u002Fheader>\n      \u003Csection class=\"insight-takeaways\">\n        \u003Ch2>What you'll take away\u003C\u002Fh2>\n        \u003Cul>\u003Cli>The key to enterprise AI landing is not model capabilities, but the matching of responsibility boundaries with the cost of errors\u003C\u002Fli>\u003Cli>Design-time generates configuration and a person publishes it. Runtime queries, exports, and completes reviews within that person's own permissions.\u003C\u002Fli>\u003Cli>The two must be separated as they have very different requirements for permissions, auditing and traceability\u003C\u002Fli>\u003Cli>The implementation is recommended to first accelerate the configuration of the human-in-the-loop, and after the data governance is in place, then advance the business inquiry metrics.\u003C\u002Fli>\u003Cli>Three non-negotiable lines: writes go only through the business API and stay inside permissions, do not bypass permissions, and do not leave the audit trail.\u003C\u002Fli>\u003C\u002Ful>\n      \u003C\u002Fsection>\n\n      \u003Cdiv class=\"insight-layout\">\n        \u003Cdiv class=\"insight-body\">\n\u003Ch2 id=\"一个窗口通吃-听起来省事-出事时没人签字\">\"A one-stop shop\" sounds convenient, but when something goes wrong, no one will sign off.\u003C\u002Fh2>\n\u003Cp>There is a common line in project reports: 'We need an assistant that can both map processes using natural language and answer operational data questions on the spot.' The demos are flashy: modify a nonconforming product handling flow, then ask for the three suppliers with the highest defect rates last month—all in the same chat window. IT later realized that the permissions matrix couldn’t be implemented—who approved the configuration changes? Which original documents do the answers correspond to? Was there any unauthorized access to factory data? When it comes to accountability, saying 'it was an AI recommendation' becomes a gray box that no one wants to take responsibility for.\u003C\u002Fp>\n\u003Cp>AI project failures in companies are rarely because the model isn’t smart enough; it’s usually because it was placed in the wrong position: tasked with both 'building systems' and 'making business judgments.' If the model is built incorrectly, you can fix it and redeploy; if the decision-making is wrong, goods are already shipped, payments made, and complaints have arrived. The costs of mistakes are asymmetric, so you can’t use the same set of entry points, permissions, and audit designs. When 'convenience' is mistaken for 'correctness,' audits and rollbacks are the first to break. This isn’t product marketing language, but a structural fact stated in the platform capabilities page — configuration and permissions are still completed on the design side; runtime queries and reviews use only published page permissions and do not write the database directly; the two are not the same chat window. If you choose the wrong entry point, changing the model later won’t save you. First establish the boundaries of responsibility, then discuss the list of model capabilities — boundaries come before parameters, and before any demo scripts.\u003C\u002Fp>\n\u003Ch2 id=\"两类职责-一个改对象-一个读事实\">Two jobs: one changes configuration, one queries and transacts within authorization.\u003C\u002Fh2>\n\u003Cp>It is more reliable to understand it according to the division of labor of 'dual agents' than according to the capability list of large models.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Design Side (Implementation)\u003C\u002Fstrong>: Service implementation, business, and secondary development. Build\u002Fmodify applications, models, forms, reports, processes, and pages using natural language; the output is\u003Cstrong>Real object that can enter the designer for further adjustment\u003C\u002Fstrong>, not a block of instructions that cannot be implemented. For example, describing 'Nonconforming product disposal: discovery — review — rework\u002Fscrap\u002Fconcession — approval — closure,' the system provides a structured draft, which is then confirmed by a person and slightly adjusted in the designer before being released.\u003C\u002Fp>\n\u003Cp>\u003Cstrong>Query side (query and handling)\u003C\u002Fstrong>Serves managers and business owners. On the live system they query, spot exceptions, and get suggestions. Creating records and completing reviews goes through the business API, stays within that person's page permissions, does not change configuration, and does not write the database directly. For example, the three suppliers with the highest incoming defect rate last month and the main defect types: the answer must drill to the source records.\u003C\u002Fp>\n\u003Cdiv class=\"table-scroll\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Dimension\u003C\u002Fth>\u003Cth>Design side\u003C\u002Fth>\u003Cth>Ask the number side\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Who uses\u003C\u002Ftd>\u003Ctd>Implementation, Business, Secondary Development\u003C\u002Ftd>\u003Ctd>Management and business leaders\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Output\u003C\u002Ftd>\u003Ctd>Draft of objects accessible to the designer\u003C\u002Ftd>\u003Ctd>Query exports, plus records or review results written through the API\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Person confirmed\u003C\u002Ftd>\u003Ctd>It must be published to take effect\u003C\u002Ftd>\u003Ctd>Queries must be traceable; a review is that person's own action\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Permission\u003C\u002Ftd>\u003Ctd>Design and Configuration Rights\u003C\u002Ftd>\u003Ctd>Inherits that person's page permissions. Queries do not exceed scope, and reviews do not skip the node.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Audit\u003C\u002Ftd>\u003Ctd>Who confirmed which change\u003C\u002Ftd>\u003Ctd>What was asked, what was handled, and the data scope\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Cost of Error\u003C\u002Ftd>\u003Ctd>Relatively low, can be revised and resent\u003C\u002Ftd>\u003Ctd>Relatively high, may affect funds and commitments\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>An 'assistant' that only spits out text and requires the implementer to redo everything in the designer is just for chatting, not for design. The proper workflow is: natural language describes intent and constraints → generate a draft object that can be opened → human verifies boundaries in the designer (parallel, rollback, approval, etc.) → confirm release and write into change audit. The on-the-spot validation is very specific: describe your most complex change or non-compliant process, and see if the output can go directly into the designer for further modification, rather than copying and pasting it elsewhere.\u003C\u002Fp>\n\u003Cp>The value of asking for numbers does not lie in 'answering like a consultant,' but in the answer being verifiable against business documents: key figures should point to the details; time, organization, and version of the metrics should be visible; if the information is insufficient to support a decision, state it clearly, rather than fabricating a complete conclusion. What management really fears is not being unable to answer, but being wrong while appearing confident. When the dictionary of standards is not frozen, a 'professional tone' only stitches together conflicting indicators into a seemingly unified story—this erodes trust even more than not being able to answer.\u003C\u002Fp>\n\u003Ch2 id=\"挂在引擎上-而不是悬空聊天\">Hang it on the engine, not chat in the air\u003C\u002Fh2>\n\u003Cp>Agents are an acceleration layer and do not replace organization, permissions, forms, flows, reports, or integration. Design-time can help draft forms and flows, but publish still needs designer review. Runtime queries the live business database. Answers should land on concrete tables and rows, without redefining metrics or bypassing row-level permissions. Creating records and completing reviews goes only through the business API, stays within what that person can do on the page, does not write the database directly, does not approve for someone else, and does not change master data. After org or permission changes, visibility must follow the new assignment so a transferred person is not still queried under the old org. If you cannot say who confirmed a generated flow publish, which records an answer rests on, or whether a review stayed within that person's permission, it should not go to production.\u003C\u002Fp>\n\u003Cp>The three bottom lines are governance requirements, not model parameters:\u003C\u002Fp>\n\u003Col>\u003Cli>\u003Cstrong>Do not directly modify production data\u003C\u002Fstrong>——Writes go only through the business API and stay within that person's page permissions. The model must not decide freely or write the database directly.\u003C\u002Fli>\u003Cli>\u003Cstrong>Do not bypass permissions\u003C\u002Fstrong>——The visible range is consistent with the user themselves; if natural language can ask for data beyond its authority, it is a leakage channel.\u003C\u002Fli>\u003Cli>\u003Cstrong>Not detached from auditing\u003C\u002Fstrong>——Each time changes are generated and confirmed, it can answer 'who confirmed the changes' and 'which records the numbers are based on'.\u003C\u002Fli>\u003C\u002Fol>\n\u003Cp>Before writing these three items into the system and access control procedures, it's better for the pilot scope to be small. Common failure patterns are obvious at a glance: mixing responsibilities makes permissions impossible to consolidate; immediate implementation without confirmation amplifies errors; untraceable answers are eventually abandoned; permission escalation leads to leaks; inconsistent standards result in professional but incorrect responses; using the query channel to 'conveniently change the approver' bypasses the change gate. Posting failure patterns in the project charter is cheaper than pursuing accountability afterward.\u003C\u002Fp>\n\u003Ch2 id=\"先人在环-后问数-顺序比模型版本重要\">Ancestors in circles, then ask the numbers: Order is more important than model version\u003C\u002Fh2>\n\u003Cp>It is recommended to roughly follow a ninety-day schedule: In the first thirty days, select one or two form\u002Fprocess scenarios, enforce \"Generate → Human Confirmation → Designer Adjustment → Publish,\" and use delivery cycle comparison and auditable changes as completion markers; in the middle thirty days, supplement the standards dictionary, master data uniqueness, and permission matrix, and close the 'universal chat' entry; in the last thirty days, only allow question counts on already launched report domains, enforce drill-down and permission inheritance, and ensure no unauthorized events occur. Pilot scenarios also require careful consideration: on the design side, prioritize documents with complex processes but relatively stable rules (changes, nonconforming product disposition, concession acceptance), and avoid jumping straight into master data reconstruction; for question counts, prioritize quality or delivery indicators with frozen standards, and do not let the model 'unify' KPIs whose definitions are still being modified weekly. Before expanding the organizational scope, first complete a permissions spot check and baseline the indicator ledger—once model-induced illusory answers enter management meetings, the cost of correcting them later is extremely high.\u003C\u002Fp>\n\u003Cdiv class=\"table-scroll\">\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Character\u003C\u002Fth>\u003Cth>Recommended to activate\u003C\u002Fth>\u003Cth>Clearly prohibited\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Implementation \u002F Secondary development\u003C\u002Ftd>\u003Ctd>Generation and Draft Editing\u003C\u002Ftd>\u003Ctd>Skip confirmation and directly publish to production\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Business key user\u003C\u002Ftd>\u003Ctd>Limited scope draft confirmation\u003C\u002Ftd>\u003Ctd>Expand to full tenant configuration rights\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Management\u003C\u002Ftd>\u003Ctd>Inherited number of questions within my scope\u003C\u002Ftd>\u003Ctd>May I ask about changing the configuration of multiple channels\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Audit \u002F IT\u003C\u002Ftd>\u003Ctd>Read-only audit and release records\u003C\u002Ftd>\u003Ctd>Use the administrator account to ask the business numbers\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Cp>Using account partitions is more secure than sharing high-privilege tokens for querying; long-term shared tokens will distort both permission inheritance and auditing. Drafts on the design side must be separated from releases, and chat drafts are prohibited from directly overwriting the production version. On the querying side, only published metrics can be consumed, and metric definitions cannot be modified privately—the definition changes must still go through the designer and release gates.\u003C\u002Fp>\n\u003Ch2 id=\"还不到窗口时-宁可晚一个月\">If it's not yet time for the window, it's better to be a month late.\u003C\u002Fh2>\n\u003Cp>When master data is not unified (the same material has multiple codes), the permission system is unclear, there is no traceability mechanism, business rules change weekly, report standards have not been frozen, or AI is treated as a substitute for acceptance, relying on it to 'guess the requirements'—introducing smart agents in these situations will only amplify the chaos faster. When even humans can't clearly manage visibility, the assistant will just ask unauthorized questions more fluently. A self-check: without the assistant, can you still clearly state \"who can change configurations, who can see which data, and where the numbers come from\"? If you can't answer, it's not yet time to go live. It's better to open up inquiries a month later than to destroy management trust with a single wrong answer.\u003C\u002Fp>\n\u003Cp>Write human–machine division into policy: the assistant drafts and surfaces leads; people judge and own outcomes. If the permission matrix is unclear, dual agents collapse into a gray box anyone can chat with and no one can audit. JEDP Digital Platform separates design-time for landing configuration and runtime for queries and handling—config and permissions still finish in design-time—so selection can check two entries, two permission sets, two audit trails, not one flashy dialog.\u003C\u002Fp>\n\u003Ch2 id=\"下一场演示只问三句话\">The next demonstration will only ask three questions\u003C\u002Fh2>\n\u003Cp>When selecting procurement and information systems, don’t be misled by a 'conversational all-in-one assistant.' Have them demonstrate your most complex change or nonconformance process: can the product continue to be modified in the designer? Then ask about a live KPI: can the answer drill down to the original document? Finally, make an unauthorized request: is it rejected and auditable? If these three tests fail, no matter how new the model is, it shouldn’t go into production. If the vendor insists that one chat window can both modify configurations and query operational data, demand to see two sets of permission matrices and two sets of audit logs—if they can’t explain, it means the boundaries of responsibility haven’t been fully defined. Only when boundaries are clear can capability be applied properly; when boundaries are blurred, capability will only accelerate failures. Separation of duties is not a slogan; it’s a threshold for going live. Two entry points, two sets of permissions, two sets of audits—if any of these are missing, don’t rush to roll it out across the entire company.\u003C\u002Fp>\n\u003Cul>\u003Cli>JEDP Platform Capability: Dual Intelligent Agents\u003C\u002Fli>\u003Cli>Details of Dual Agents: Design State and Operating State\u003C\u002Fli>\u003Cli>Platform and Dual-Agent Sandbox Trial\u003C\u002Fli>\u003Cli>System Selection Scoring Sheet (No Information Retention)\u003C\u002Fli>\u003C\u002Ful>\n        \u003C\u002Fdiv>\n        \u003Caside class=\"insight-toc\" aria-label=\"On this page\">\n          \u003Ch2>Contents\u003C\u002Fh2>\n          \u003Ca class=\"h2\" href=\"#一个窗口通吃-听起来省事-出事时没人签字\">\"A one-stop shop\" sounds convenient, but when something goes wrong, no one will sign off.\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#两类职责-一个改对象-一个读事实\">Two jobs: one changes configuration, one queries and transacts within authorization.\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#挂在引擎上-而不是悬空聊天\">Hang it on the engine, not chat in the air\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#先人在环-后问数-顺序比模型版本重要\">Ancestors in circles, then ask the numbers: Order is more important than model version\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#还不到窗口时-宁可晚一个月\">If it's not yet time for the window, it's better to be a month late.\u003C\u002Fa>\n          \u003Ca class=\"h2\" href=\"#下一场演示只问三句话\">The next demonstration will only ask three questions\u003C\u002Fa>\n        \u003C\u002Faside>\n      \u003C\u002Fdiv>\n\n      \u003Cdiv class=\"insight-cta\">\n        \u003Cp>\u003Cstrong>Want to apply this in your plant?\u003C\u002Fstrong>Resources has matching self-assessments and templates—or book a live demo.\u003C\u002Fp>\n        \u003Cdiv class=\"insight-cta-actions\">\n          \u003Ca href=\"\u002Fresources?from=agent#selftest\" class=\"btn btn-outline\">Self-assessment in Resources\u003C\u002Fa>\n          \u003Ca href=\"\u002Fcases#case-platform\" class=\"btn btn-outline\">Platform acceptance case\u003C\u002Fa>\n          \u003Ca href=\"\u002Fcontact?from=agent\" class=\"btn\">Get a demo\u003C\u002Fa>\n        \u003C!-- next-step:BEGIN -->\u003Ca class=\"btn\" href=\"\u002Fengines\">Product solution: \u003Cspan>Platform\u003C\u002Fspan>\u003C\u002Fa>\u003Ca class=\"btn btn-outline\" href=\"\u002Fres\u002Fresources\u002Fkits\u002F15-platform-6weeks-vs-6months.html\">Matching resource kit: \u003Cspan>6 Weeks vs 6 Months: Comparison Roadmap Infographic\u003C\u002Fspan>\u003C\u002Fa>\u003C!-- next-step:END -->\n        \u003C\u002Fdiv>\n      \u003C\u002Fdiv>\n      \u003Csection class=\"insight-faq\" id=\"faq\">\n        \u003Ch2>FAQ\u003C\u002Fh2>\n        \u003Cp class=\"insight-faq-sub\">Quick answers on this topic—still stuck?\u003Ca href=\"\u002Fcontact?from=agent\">Contact\u003C\u002Fa>\u003C\u002Fp>\n        \u003Cdiv class=\"faq-list\">\n        \u003Cdetails class=\"faq-item\" open>\n          \u003Csummary>Will the design side generate incorrect models?\u003C\u002Fsummary>\n          \u003Cp>Yes, so the process must be “Proposal Generation + Manual Confirmation”. It reduces the initial cost of modeling and the risk of omission, and does not replace human judgment. All resulting changes are traced back to who confirmed them.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>What should I do if I answered the question incorrectly?\u003C\u002Fsummary>\n          \u003Cp>Two measures - one is that the answer must be able to drill down to the original record, so that the user can check it themselves; the other is that the permission inherits the user's data range, and does not expand the visible data. If the answer cannot be traced back, it should not be used as a basis for decision-making.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>Should I upgrade the configuration for speed first or upgrade the questions first?\u003C\u002Fsummary>\n          \u003Cp>It is recommended to first do the configuration assistance in the human loop. The risk is low, and the benefits are directly reflected in the shortened delivery cycle. Questioning numbers relies more heavily on data quality and consistency of standards, and it is easy to answer incorrectly when the prerequisites are insufficient.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>Will agents replace configuration personnel?\u003C\u002Fsummary>\n          \u003Cp>Not in the short term. It is more like a skilled assistant who can quickly give drafts, point out omissions, and reduce duplication of effort, but business judgment, boundary confirmation, and responsibility are still on the person.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003Cdetails class=\"faq-item\">\n          \u003Csummary>Can a chat window do both modeling and answering questions at the same time?\u003C\u002Fsummary>\n          \u003Cp>Not recommended. It may seem convenient on the surface, but in reality it mixes configuration permissions with the visibility of business data, making auditing and rollback unclear. The platform side should insist on separate entry points.\u003C\u002Fp>\n        \u003C\u002Fdetails>\n        \u003C\u002Fdiv>\n      \u003C\u002Fsection>\n      \u003Csection class=\"insight-related\">\n        \u003Ch2>Related reading\u003C\u002Fh2>\n        \u003Cdiv class=\"insight-related-grid\">\n          \u003Carticle class=\"r-card\">\n            \u003Cdiv class=\"r-tags\">SaaS · AI agents\u003C\u002Fdiv>\n            \u003Ch3>Who can ask which numbers? Keep permissions anchored to the data row\u003C\u002Fh3>\n            \u003Cp>AI query is not a new data exit: the runtime agent trims rows and columns by the permission groups configured when the list page was published - applicable people, data scope and field permissions share the same source as the list page, and out-of-scope requests must be rejected and logged. This article explains how permission groups are resolved and where the fallback lands, risk tiers for export and write actions, a rollout order and six demonstrable acceptance checks.\u003C\u002Fp>\n            \u003Cdiv class=\"r-meta\">2026-10-23 · about 11 min\u003C\u002Fdiv>\n          \u003C\u002Farticle>\n          \u003Carticle class=\"r-card\">\n            \u003Cdiv class=\"r-tags\">Digital foundation\u003C\u002Fdiv>\n            \u003Ch3>Choosing a digital foundation: set the yardstick with these 8 questions before talking price\u003C\u002Fh3>\n            \u003Cp>When the manufacturing industry chooses a digital foundation, set the benchmark first before discussing the price. Cover the modeling limits, process engine, data ownership, secondary development boundaries, and more with 8 questions that can be verified on the spot…\u003C\u002Fp>\n            \u003Cdiv class=\"r-meta\">2026-08-13 · ~9 min\u003C\u002Fdiv>\n          \u003C\u002Farticle>\n          \u003Carticle class=\"r-card\">\n            \u003Cdiv class=\"r-tags\">Digital · Implementation\u003C\u002Fdiv>\n            \u003Ch3>Digital ROI: replace gut feel with four measurable benefit types\u003C\u002Fh3>\n            \u003Cp>The ROI debate has stalled, mostly because the metrics aren't aligned. Starting from the project kickoff meeting, the four types of verifiable benefits — manpower, scrap rework, compliance audits, and cash delivery — the seven overlooked cost items, and the cost structure differences between base and customized solutions were presented...\u003C\u002Fp>\n            \u003Cdiv class=\"r-meta\">2026-09-24 · ~9 min\u003C\u002Fdiv>\n          \u003C\u002Farticle>\n        \u003C\u002Fdiv>\n      \u003C\u002Fsection>\n    \u003C\u002Fdiv>\n  \u003C\u002Farticle>",1793937990360]