这是三层台阶 C 层 · 开发元能力的典型场景。传统代理机构的案件管理 ERP，是静态表单 + 人工录入 + 期限靠人盯 + 数据各部门孤岛。AI Native 升级 = 用 Claude Code 把它长成每个模块都嵌着智能体、能随业务持续进化、数据不出所的自有系统——不是买一套外部 SaaS，而是把“用 AI 造工具”长在公司自己手里。A typical layer-C · meta-capability scenario. A traditional agency's case-management ERP is static forms + manual entry + human-watched deadlines + siloed data. The AI-Native upgrade uses Claude Code to grow it into a self-owned system where every module has an embedded agent, it evolves with the business, and data never leaves the firm — not buying an external SaaS, but growing "building tools with AI" in-house.
代理机构每天跑在案件全生命周期上：立案 → 受理 → 审查 → OA 答复 → 授权 → 年费监控。传统 ERP 把这些变成一堆要人工填的表，真正吃人的地方它一个都没接住。An agency runs the full case lifecycle daily: filing → acceptance → examination → office-action response → grant → annuity monitoring. A traditional ERP turns these into forms people fill by hand — and catches none of the parts that actually hurt.
OA 答复期、年费、PCT 国家阶段、优先权——分散在各国各案，漏一个就是行业头号事故，全凭经办人记性和 Excel。OA response dates, annuities, PCT national phase, priority — scattered across jurisdictions and cases. Miss one and it's the industry's worst accident, yet it all rides on memory and spreadsheets.
交底书、受理通知、OA 通知的著录项靠人逐字录入；财务、专利、涉外各管一摊，查一个案件的全貌要问一圈。Bibliographic data from disclosures and notices is keyed in by hand; finance, patent and foreign teams each hold a piece — getting a full case picture means asking around.
怎么分诊一份 OA、怎么算计件提成、怎么做冲突检查——经验在资深代理师脑子里，新人接不住、人走了带走。How to triage an OA, compute piece-rate commission, run a conflict check — the experience sits in senior agents' heads; rookies can't pick it up and it walks out the door.
同一套案件数据底座之上，七个垂直智能体分别接住最吃人的环节。AI 出草稿、人定稿是贯穿始终的铁律。On one shared case-data foundation, seven vertical agents each take on the most painful steps. AI drafts, humans decide is the rule throughout.
传统：Excel + 经办人记性。升级后：自动盯各国各案的 OA 答复期 / 年费 / PCT 国家阶段 / 优先权，按紧迫度分级预警并直接推到经办人微信。漏期这件头号事故，从"靠人"变"靠系统"。Before: spreadsheets + memory. After: auto-tracks OA dates / annuities / PCT national phase / priority across cases, raises tiered alerts by urgency, and pushes them to the handler's WeChat. The #1 accident — missed deadlines — moves from "on people" to "on the system."
传统：资深代理师逐份读。升级后：把一份审查意见拆成审查员核心质疑点 + 引用对比文件 + 待答复争点清单，起草针对"创造性质疑"的答复思路提纲（区别技术特征、技术效果）——AI 出思路、代理师定论。Before: a senior agent reads each one. After: breaks an office action into the examiner's core objections + cited references + a list of points to rebut, and drafts a response outline for inventiveness challenges (distinguishing features, technical effects) — AI outlines, the agent decides.
传统：著录项逐字手敲。升级后：从交底书 / 受理通知 / OA 通知自动结构化提取著录项、期限、费用，写进案件库，人工只做复核。录入工作量塌缩，数据从源头就是干净的。Before: bibliographic fields typed by hand. After: auto-extracts fields, deadlines and fees from disclosures / acceptance & office-action notices into the case database; people only review. Data-entry collapses and data is clean at the source.
传统：出纳手工核对。升级后：官费 + 代理费自动勾稽，按代理人案件量自动核算计件提成，整理收付款台账与异常清单。财务从"对账"变"审核异常"。Before: the cashier reconciles by hand. After: auto-reconciles official fees + agent fees, computes piece-rate commission from each agent's caseload, and builds the ledger and exception list. Finance moves from reconciling to reviewing exceptions.
传统：查个案件全貌要跨部门问一圈。升级后：自然语言一句话跨部门检索——"客户 X 所有在审案件及最近一个期限是什么"，直接出答案，不再各管一摊。Before: a full case picture means asking around. After: one natural-language query does the cross-department lookup — "all of client X's pending cases and the next deadline" — answered directly, no more silos.
传统：靠记忆和翻档。升级后：新案自动对照在档客户 / 对手做冲突检查初筛，列出疑点供合规复核——是否真冲突仍由人定。Before: from memory and digging through files. After: a new case is auto-screened against existing clients / adversaries for conflicts, surfacing flags for compliance review — whether it's a real conflict stays a human call.
传统：涉外代理师跨 7–8 个技术领域，逐封手写英文邮件。升级后：起草发给国外代理所的催复 / 询官费 / 指示答复英文邮件；把不同技术领域的客户咨询快速归类并起草初步回复，让涉外环节从"瓶颈"变"流水线"。Before: a foreign-affairs agent spanning 7–8 technical fields writes each English email by hand. After: drafts English emails to foreign associates (chasers / fee queries / response instructions) and quickly classifies client inquiries across fields with first-draft replies — turning the foreign desk from a bottleneck into a pipeline.
外部 SaaS 把你锁死在它的数据结构和迭代节奏里；用 Claude Code 自己长出来的系统不一样——External SaaS locks you into its data model and release cadence. A system you grow yourself with Claude Code is different —
新增一个技术领域、一类案件、一条计费规则——机构自己加模块、改流程，不用等供应商排期。A new technical field, case type or billing rule — the firm adds modules and changes flows itself, no vendor roadmap to wait on.
交底书、案件材料、客户信息私有化处理，符合代理行业的保密底线，没有"上云"顾虑。Disclosures, case files and client data are handled privately — meeting the profession's confidentiality bar, with no cloud worry.
资深代理师的分诊、答复、计费经验被结构化进系统，新人接得住、人走带不走。Senior agents' triage, response and billing know-how is structured into the system — rookies can use it, leavers can't take it.
最终留在机构的，是用 Claude Code 持续造工具、改系统的开发元能力本身——这才是别人抢不走的护城河。What stays is the firm's own meta-capability to keep building tools and reshaping systems with Claude Code — the moat no one can take.
新颖性 / 创造性认定、OA 答复定稿、侵权认定、质量抽检结论——AI 只做信息梳理与争点清单，结论由代理师 / 法务作出。Novelty / inventiveness calls, final OA responses, infringement findings, QA conclusions — AI only organizes information and issue lists; the agent / counsel concludes.
合同 / 用印审核、案件报价定价涉及法律责任与专业判断，AI 生成检查清单与风险提示，管理者判定。Contract / seal review and case pricing carry legal liability and judgment; AI produces checklists and risk flags, management decides.
交底书等保密材料注意脱敏与私有部署边界，案件数据不出所——这是代理行业不可逾越的底线。Disclosures and other confidential materials are redacted within a private-deployment boundary; case data stays in the firm — a line the profession can't cross.
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请提供贵机构的案件流程与现有系统，我们将提供从单一模块切入的 AI Native 升级方案。Share your firm's case workflow and current system, and we'll propose an AI-Native upgrade that begins with a single module.
