{
  "schemaVersion": "0.1.0",
  "profileId": "default",
  "generatedAt": "2026-07-14T15:05:26.057Z",
  "profile": {
    "id": "default",
    "name": {
      "zh": "George Zhang",
      "en": "George Zhang"
    },
    "headline": {
      "zh": "企业级 Agent 平台架构负责人",
      "en": "Enterprise Agent Platform Architect"
    },
    "summary": {
      "zh": "10 年大型平台与 AI 工程经验，历经阿里、商汤，现负责企业级 Agent 平台架构、产品化与团队交付。",
      "en": "10 years across large-scale platforms and AI engineering at Alibaba and SenseTime, now leading enterprise Agent platform architecture, productization, and delivery."
    },
    "yearsOfExperience": 10,
    "location": {
      "zh": "中国",
      "en": "China"
    },
    "avatar": "avatar.png",
    "companies": [
      {
        "name": "YC",
        "type": "work"
      },
      {
        "name": "Alibaba",
        "type": "work"
      },
      {
        "name": "SenseTime",
        "type": "work"
      }
    ],
    "targetRoles": [
      {
        "id": "agent-platform",
        "priority": 1
      }
    ],
    "links": {
      "email": "sunionpeaks@gmail.com",
      "github": "https://github.com/Nunchakus888",
      "linkedin": "",
      "maimai": "",
      "huggingface": "",
      "website": "https://sunionpeaks.pages.dev"
    },
    "availability": {
      "status": {
        "zh": "企业级 Agent 平台架构与技术负责人",
        "en": "Enterprise Agent Platform Architecture and Engineering Leadership"
      },
      "preferredLocations": [
        "China"
      ],
      "expectedPackage": {
        "zh": "平台级职责、核心业务场景、长期技术影响力与跨团队协作",
        "en": "Platform-level ownership, core business scenarios, long-term technical impact, and cross-functional collaboration"
      }
    },
    "localized": {
      "zh": {
        "locale": "zh",
        "name": "George Zhang",
        "headline": "企业级 Agent 平台架构负责人",
        "summary": "10 年大型平台与 AI 工程经验，历经阿里、商汤，现负责企业级 Agent 平台架构、产品化与团队交付。",
        "location": "中国",
        "availability": "企业级 Agent 平台架构与技术负责人"
      },
      "en": {
        "locale": "en",
        "name": "George Zhang",
        "headline": "Enterprise Agent Platform Architect",
        "summary": "10 years across large-scale platforms and AI engineering at Alibaba and SenseTime, now leading enterprise Agent platform architecture, productization, and delivery.",
        "location": "China",
        "availability": "Enterprise Agent Platform Architecture and Engineering Leadership"
      }
    }
  },
  "roles": [
    {
      "id": "agent-platform",
      "path": "agent-platform",
      "title": {
        "zh": "企业级 Agent 平台架构负责人",
        "en": "Enterprise Agent Platform Architect"
      },
      "headline": {
        "zh": "从 Runtime 到生产运营的 Agent 平台架构",
        "en": "Agent Platform Architecture from Runtime to Production Operations"
      },
      "summary": {
        "zh": "负责 Agent Runtime、工具与权限、评测回放、质量运营和企业系统集成，推动大模型能力稳定进入核心业务流程。",
        "en": "Owning Agent Runtime, tools and permissions, replay evaluation, quality operations, and enterprise integration to bring LLM capabilities reliably into core workflows."
      },
      "focusAreas": [
        "Agent Runtime",
        "Agentic RAG",
        "Tool-use / MCP",
        "Permission & Audit",
        "Evaluation & LLMOps",
        "Enterprise Integration"
      ],
      "relatedCases": [
        "enterprise-agent-platform",
        "aigc-saas-platform",
        "commerce-platform-engineering",
        "posttraining-data-flywheel"
      ],
      "relatedLabs": [
        "eval-harness"
      ],
      "relatedWriting": [
        "agent-platform-positioning-assessment"
      ],
      "platformProfile": {
        "badge": "PLATFORM OWNER",
        "title": {
          "zh": "平台职责",
          "en": "Platform Ownership"
        },
        "summary": {
          "zh": "覆盖架构演进、核心运行时、平台公共能力、生产质量运营和团队交付，对稳定性、效率、成本与业务结果负责。",
          "en": "End-to-end ownership across architecture evolution, core runtime, shared platform capabilities, production quality operations, and team delivery, accountable for reliability, efficiency, cost, and business outcomes."
        },
        "sourceWriting": "agent-platform-positioning-assessment",
        "capabilities": [
          {
            "zh": "主导 Agent 从代码引导 1.0 演进为模型驱动 2.0，明确模型决策与 Runtime 控制边界。",
            "en": "Led the evolution from code-guided Agent 1.0 to model-driven 2.0, defining the boundary between model decisions and Runtime control."
          },
          {
            "zh": "建设 Agentic RAG、多 Agent 并行编排、统一上下文、动态 Skill 路由与 MCP/API 工具体系。",
            "en": "Built Agentic RAG, parallel multi-agent orchestration, unified context, dynamic Skill routing, and MCP/API tool systems."
          },
          {
            "zh": "建立多租户隔离、工具权限、审计、人工接管、幂等重试与高风险操作约束。",
            "en": "Established multi-tenant isolation, tool permissions, audit, human handoff, idempotent retries, and high-risk action controls."
          },
          {
            "zh": "贯通生产 trace、回放评测、发布门禁与数据反馈，将线上问题转化为知识、Skill 和策略迭代。",
            "en": "Connected production traces, replay evaluation, release gates, and data feedback to turn production issues into knowledge, Skill, and policy improvements."
          },
          {
            "zh": "带领 Agent 小组交付核心平台能力，并以 AI 开发 Harness 推动 40 个仓库形成一致的工程治理。",
            "en": "Led the Agent squad in core platform delivery and used an AI development harness to establish consistent engineering governance across 40 repositories."
          }
        ],
        "dimensions": [
          {
            "title": {
              "zh": "Runtime 与编排",
              "en": "Runtime and Orchestration"
            },
            "summary": {
              "zh": "Agentic RAG、多 Agent 并行编排、统一上下文与动态 Skill 路由；平均 7k+ tokens 对话负载下端到端约 7s。",
              "en": "Agentic RAG, parallel multi-agent orchestration, unified context, and dynamic Skill routing, with about 7s end-to-end latency under 7k+ average conversation-token load."
            }
          },
          {
            "title": {
              "zh": "工具与生产治理",
              "en": "Tools and Production Governance"
            },
            "summary": {
              "zh": "覆盖 MCP/API、tool gating、多租户权限、审计、side-effect 控制、人工接管与可观测账本。",
              "en": "MCP/API, tool gating, multi-tenant permissions, audit, side-effect controls, human handoff, and an observable execution ledger."
            }
          },
          {
            "title": {
              "zh": "质量与成本运营",
              "en": "Quality and Cost Operations"
            },
            "summary": {
              "zh": "以回放评测、content gap 和 usage 审计驱动持续优化，token 消耗下降 60%，自动解决率提升约 30%。",
              "en": "Continuous optimization through replay evaluation, content gaps, and usage auditing, reducing token consumption by 60% and improving auto-resolution by about 30%."
            }
          },
          {
            "title": {
              "zh": "团队与平台推广",
              "en": "Team and Platform Adoption"
            },
            "summary": {
              "zh": "带领 3 人 Agent 小组，协同 20 人产研团队；AI 开发 Harness 覆盖 40 个仓库。",
              "en": "Led a 3-person Agent squad within a 20-person product-engineering team; expanded the AI development harness across 40 repositories."
            }
          }
        ]
      }
    }
  ],
  "metrics": [
    {
      "label": {
        "zh": "服务规模",
        "en": "Service scale"
      },
      "value": {
        "zh": "2k+ 租户 / 23w+ 用户",
        "en": "2k+ tenants / 230k+ users"
      },
      "description": {
        "zh": "生产级跨行业 AI Agent 客服 SaaS 平台。",
        "en": "Production-grade cross-industry AI Agent customer-service SaaS platform."
      }
    },
    {
      "label": {
        "zh": "峰值请求",
        "en": "Peak traffic"
      },
      "value": {
        "zh": "6w / day",
        "en": "60k / day"
      },
      "description": {
        "zh": "生产环境峰值，端到端平均响应约 7s。",
        "en": "Production peak, with about 7s average end-to-end response time."
      }
    },
    {
      "label": {
        "zh": "团队范围",
        "en": "Team scope"
      },
      "value": {
        "zh": "20 人产研 / 3 人 Agent 小组",
        "en": "20-person product-engineering / 3-person Agent squad"
      },
      "description": {
        "zh": "带领 3 人 Agent 小组，协同 20 人产研团队。",
        "en": "Led a 3-person Agent squad within a 20-person product-engineering team."
      }
    },
    {
      "label": {
        "zh": "Harness 覆盖",
        "en": "Harness coverage"
      },
      "value": {
        "zh": "40 个仓库",
        "en": "40 repositories"
      },
      "description": {
        "zh": "覆盖 40 个代码仓库的 AI 辅助开发治理。",
        "en": "AI-assisted development governance across 40 repositories."
      }
    }
  ],
  "experience": [
    {
      "company": {
        "zh": "YC",
        "en": "YC"
      },
      "role": {
        "zh": "Agent 平台负责人",
        "en": "Agent Platform Lead"
      },
      "period": {
        "zh": "2025-03 - 现在",
        "en": "2025-03 - Present"
      },
      "location": {
        "zh": "中国",
        "en": "China"
      },
      "summary": {
        "zh": "负责跨行业 AI Agent 客服 SaaS 平台从架构演进到生产运营，覆盖 2k+ 租户、23w+ 用户、峰值 6w 请求/天；统筹 Runtime、工具与 Skill、RAG/Memory、质量评测、成本治理和团队交付。",
        "en": "Owned a cross-industry AI Agent customer-service SaaS platform from architecture evolution through production operations, covering 2k+ tenants, 230k+ users, and 60k peak requests/day across Runtime, tools and Skills, RAG/Memory, quality evaluation, cost governance, and team delivery."
      },
      "featuredProject": {
        "title": {
          "zh": "模型驱动 Agent Runtime 2.0",
          "en": "Model-driven Agent Runtime 2.0"
        },
        "summary": {
          "zh": "面向 2k+ 租户、23w+ 用户的客服 SaaS，重构 Agentic RAG、多轮工具调用、动态 Skill 与多 Agent 统一上下文；在平均 7k+ tokens 对话负载下将端到端响应稳定在约 7s，token 消耗下降 60%，支撑客户问题自动解决率达到 70%+。",
          "en": "Re-architected Agentic RAG, multi-turn tools, dynamic Skills, and unified multi-agent context for 2k+ tenants and 230k+ users; sustained about 7s end-to-end latency at an average 7k+ tokens per conversation, cut token use by 60%, and supported 70%+ auto-resolution."
        }
      },
      "highlights": [
        {
          "zh": "建立覆盖请求、执行阶段、工具调用与终态回传的可观测和评测链路，以 correlation_id、trace、token usage、回放集和发布门禁定位质量、时延与成本问题。",
          "en": "Established end-to-end observability and evaluation across requests, execution phases, tool calls, and terminal callbacks, using correlation IDs, traces, token usage, replay sets, and release gates to isolate quality, latency, and cost issues."
        },
        {
          "zh": "建设未知问题聚类与主题发现平台，将每周约 1w+ 至 6w+ 条问题沉淀为 content gap；约 50% 聚类结果进入知识库/RAG/Skill 迭代，高频主题处理覆盖约 70%，自动解决率提升约 30%。",
          "en": "Built an unknown-question clustering and topic-discovery platform that turned about 10k+ to 60k+ weekly questions into content gaps; about 50% of clusters fed knowledge base/RAG/Skill iteration, high-frequency topic handling reached about 70%, and auto-resolution improved by about 30%."
        },
        {
          "zh": "带领 3 人 Agent 小组推进 Runtime 服务化拆分与平台交付，统一网关鉴权、服务契约、容器构建和发布规范；推动 AI 开发 Harness 覆盖 40 个仓库，形成 Spec/EPIC、验收门禁、自检脚本和 PR 模板。",
          "en": "Led a 3-person Agent squad through Runtime service decomposition and platform delivery, standardizing gateway authentication, service contracts, container builds, and release conventions; expanded the AI development harness across 40 repositories with Spec/EPIC workflows, acceptance gates, self-check scripts, and PR templates."
        }
      ],
      "tags": [
        "Agent Runtime",
        "LLMOps",
        "Evaluation",
        "Tool-use",
        "AI Engineering Harness",
        "FastAPI",
        "Milvus"
      ]
    },
    {
      "company": {
        "zh": "自主创业",
        "en": "Independent Venture"
      },
      "role": {
        "zh": "AI 平台架构与全栈研发",
        "en": "AI Platform Architecture & Full-stack Engineering"
      },
      "period": {
        "zh": "2024-05 - 2025-02",
        "en": "2024-05 - 2025-02"
      },
      "location": {
        "zh": "中国",
        "en": "China"
      },
      "summary": {
        "zh": "围绕 AI 数据与模型服务产品开展独立研发和项目交付，负责产品定义、系统架构、全栈实现、容器化部署与生产迭代。",
        "en": "Built and delivered independent AI data and model-serving products, owning product definition, system architecture, full-stack implementation, containerized deployment, and production iteration."
      },
      "featuredProject": {
        "title": {
          "zh": "多模态数据标注与模型服务平台",
          "en": "Multimodal Data Labeling and Model-serving Platform"
        },
        "summary": {
          "zh": "从 0 到 1 打通人工标注、训练数据、模型产物、自动标注与质量校验闭环；以 React Canvas、Django、PostgreSQL 和 Redis 承载任务与数据链路，通过 Nuclio、Traefik 封装可插拔 Serverless 推理服务，完成音视频、图像任务的企业生产环境交付。",
          "en": "Built the loop from human annotation and training data through model artifacts, auto-labeling, and quality validation; combined React Canvas, Django, PostgreSQL, and Redis with pluggable Nuclio/Traefik serverless inference to deliver production audio, video, and image workflows."
        }
      },
      "highlights": [],
      "tags": [
        "AI Data Platform",
        "Model Serving",
        "Serverless",
        "Django",
        "React"
      ]
    },
    {
      "company": {
        "zh": "商汤科技",
        "en": "SenseTime"
      },
      "role": {
        "zh": "AIGC SaaS 架构师",
        "en": "AIGC SaaS Architect"
      },
      "period": {
        "zh": "2021-09 - 2024-05",
        "en": "2021-09 - 2024-05"
      },
      "location": {
        "zh": "上海",
        "en": "Shanghai"
      },
      "summary": {
        "zh": "负责 AI 绘画、模型训练与平台化产品的 Web 工程和微服务架构，参与生成式 AI SaaS 从 0 到 1 建设。",
        "en": "Owned web engineering and microservice architecture for AI image generation, model training, and platform products, contributing to 0-to-1 generative AI SaaS delivery."
      },
      "featuredProject": {
        "title": {
          "zh": "妙画 AIGC SaaS 与模型服务链路",
          "en": "Miaohua AIGC SaaS and Model-serving Workflow"
        },
        "summary": {
          "zh": "面向自研作画模型、LoRA 训练和第三方开源模型，主导前后端架构与服务拆分，将 Stable Diffusion / PyTorch 推理、任务队列、模型文件、结果后处理和日志监控组织为可扩展的异步生产链路，支撑 Web、H5 与小程序交付。",
          "en": "Led architecture and service decomposition for proprietary image models, LoRA training, and open-source integration, organizing Stable Diffusion / PyTorch inference, queues, model files, post-processing, and observability into a scalable asynchronous workflow across Web, H5, and mini-program clients."
        }
      },
      "highlights": [],
      "tags": [
        "AIGC SaaS",
        "Stable Diffusion",
        "PyTorch",
        "FastAPI",
        "Microservices",
        "DevOps"
      ]
    },
    {
      "company": {
        "zh": "阿里巴巴集团",
        "en": "Alibaba Group"
      },
      "role": {
        "zh": "高级 Web 前端工程师",
        "en": "Senior Web Frontend Engineer"
      },
      "period": {
        "zh": "2017-10 - 2021-08",
        "en": "2017-10 - 2021-08"
      },
      "location": {
        "zh": "上海 / 杭州",
        "en": "Shanghai / Hangzhou"
      },
      "summary": {
        "zh": "先后负责本地生活商业推广与淘宝新零售技术团队的高流量跨端工程，覆盖广告投放与归因、直播互动、微前端、SSR 和性能优化。",
        "en": "Worked across Alibaba Local Services commercial promotion and Taobao new-retail engineering on high-traffic cross-platform systems spanning ad delivery and attribution, live interaction, micro-frontends, SSR, and performance."
      },
      "featuredProject": {
        "title": {
          "zh": "淘宝直播跨端互动与微前端平台",
          "en": "Taobao Live Cross-platform Interaction and Micro-frontend Platform"
        },
        "summary": {
          "zh": "主导直播间 Web 互动层从 Weex 单组件模式升级为 H5 微前端编排，统一布局、路由、事件总线和通信协议，并通过 SSR 同构渲染与分级资源策略将 FCP 稳定在 1.5s 以内，支撑高流量、弱网环境下的多业务组件交付。",
          "en": "Led the live-room interaction layer from isolated Weex components to H5 micro-frontend orchestration, unifying layout, routing, event bus, and communication contracts; combined SSR and tiered resources to keep FCP below 1.5s under high traffic and weak networks."
        }
      },
      "highlights": [
        {
          "zh": "设计基于 shell / iframe 的微前端架构，统一多业务子应用通信、路由定义、静态资源加载和部署方式。",
          "en": "Designed a shell / iframe based micro-frontend architecture to standardize sub-application communication, routing, static resource loading, and deployment."
        },
        {
          "zh": "沉淀 Vue / React 脚手架、跨平台 JsBridge、广告日志上报 SDK 和通用组件库，参与下单、广告结算、订单归因到商家经营分析的关键链路建设。",
          "en": "Built Vue / React scaffolds, cross-platform JsBridge, ad logging SDK, and reusable components, contributing to key flows from ordering and ad settlement to attribution and merchant analysis."
        }
      ],
      "tags": [
        "High-traffic Web",
        "SSR",
        "Performance",
        "Ads Platform",
        "Attribution",
        "JsBridge",
        "SDK",
        "Micro-frontend"
      ]
    },
    {
      "company": {
        "zh": "宁波森浦融讯",
        "en": "Ningbo Senpu Rongxun"
      },
      "role": {
        "zh": "全栈开发工程师",
        "en": "Full-stack Developer"
      },
      "period": {
        "zh": "2016-05 - 2017-09",
        "en": "2016-05 - 2017-09"
      },
      "location": {
        "zh": "宁波",
        "en": "Ningbo"
      },
      "summary": {
        "zh": "负责金融报表、Java Web 服务和前端迁移，完成 AngularJS 到 Vue 的客户端迁移和 Web UI 组件库建设。",
        "en": "Owned financial reporting, Java web services, and frontend migration from AngularJS to Vue with a shared Web UI component library."
      },
      "featuredProject": {
        "title": {
          "zh": "金融报表与业务管理平台",
          "en": "Financial Reporting and Operations Platform"
        },
        "summary": {
          "zh": "基于 Spring Boot、MyBatis 建设金融报表数据汇总与业务管理服务，完成 AngularJS 到 Vue 的渐进式迁移并沉淀共享 Web UI 组件，形成跨端平台架构与全栈交付的工程基础。",
          "en": "Built financial-reporting and operations services with Spring Boot and MyBatis, completed the AngularJS-to-Vue migration, and established a shared Web UI component library for cross-platform delivery."
        }
      },
      "highlights": [],
      "tags": [
        "Java",
        "Spring Boot",
        "Vue",
        "Full-stack"
      ]
    }
  ],
  "skills": [
    {
      "id": "agent-platform",
      "title": {
        "zh": "Agent 平台架构",
        "en": "Agent Platform Architecture"
      },
      "description": {
        "zh": "覆盖模型驱动运行时、Agentic RAG、多 Agent 编排、上下文与工具执行。",
        "en": "Model-driven runtime, Agentic RAG, multi-agent orchestration, context, and tool execution."
      },
      "skills": [
        "Agent Runtime",
        "Agentic RAG / Memory",
        "Multi-agent Orchestration",
        "Workflow Orchestration",
        "Tool-use / Function Calling",
        "MCP / Skills",
        "Human-in-the-loop"
      ]
    },
    {
      "id": "production-governance",
      "title": {
        "zh": "生产治理与质量运营",
        "en": "Production Governance and Quality Operations"
      },
      "description": {
        "zh": "将多租户、权限、审计、评测、可观测和成本治理纳入统一运行边界。",
        "en": "Unified operating boundaries for tenancy, permission, audit, evaluation, observability, and cost."
      },
      "skills": [
        "Multi-tenant Isolation",
        "Permission Model",
        "Tool Registry",
        "Audit Trail / Guardrails",
        "Replay Evaluation / Golden Set",
        "Trace Observability",
        "Usage / Cost Governance"
      ]
    },
    {
      "id": "platform-engineering",
      "title": {
        "zh": "平台工程与企业集成",
        "en": "Platform Engineering and Enterprise Integration"
      },
      "description": {
        "zh": "覆盖服务端、数据与向量检索、微服务、云基础设施和企业系统接入。",
        "en": "Backend, data and vector retrieval, microservices, cloud infrastructure, and enterprise integration."
      },
      "skills": [
        "Python / FastAPI",
        "TypeScript / Node.js",
        "Rust / Java",
        "Linux",
        "PostgreSQL / Redis / Milvus",
        "Docker / Kubernetes",
        "Microservices / CI/CD",
        "Alibaba Cloud / Google Cloud"
      ]
    },
    {
      "id": "model-data-engineering",
      "title": {
        "zh": "模型与数据工程",
        "en": "Model and Data Engineering"
      },
      "description": {
        "zh": "面向多模型接入、上下文工程、检索质量、推理链路与数据反馈闭环。",
        "en": "Multi-model integration, context engineering, retrieval quality, inference workflows, and data feedback loops."
      },
      "skills": [
        "Model APIs / Routing",
        "Context Engineering",
        "RAG Retrieval / Reranking",
        "PyTorch / Stable Diffusion",
        "Gemini",
        "Data Feedback Loops",
        "LLMOps"
      ]
    },
    {
      "id": "engineering-leadership",
      "title": {
        "zh": "技术领导与工程治理",
        "en": "Engineering Leadership and Governance"
      },
      "description": {
        "zh": "通过架构规则、规格驱动、验收门禁和跨团队协作推进平台稳定演进。",
        "en": "Stable platform evolution through architecture rules, specification-driven delivery, acceptance gates, and cross-functional collaboration."
      },
      "skills": [
        "Platform Roadmap",
        "Architecture Rules",
        "Spec / EPIC Workflow",
        "Release Gates",
        "Cross-functional Delivery",
        "AI Development Harness",
        "Team Standards"
      ]
    }
  ],
  "content": {
    "zh": {
      "cases": [
        {
          "slug": "posttraining-data-flywheel",
          "title": "Agent 平台数据反馈机制",
          "summary": "连接生产 trace、线上问题样本、评测样本和偏好数据，将线上行为转化为持续质量改进机制。",
          "tags": [
            "Preference Data",
            "DPO",
            "Evaluation"
          ],
          "featured": false,
          "url": "/zh/work/posttraining-data-flywheel"
        },
        {
          "slug": "enterprise-agent-platform",
          "title": "生产级 Agent 平台架构",
          "summary": "负责跨行业 AI Agent 客服 SaaS 平台从架构演进到生产运营，并推动团队 AI 开发 Harness 覆盖 40 个仓库。",
          "tags": [
            "Agent Runtime",
            "Tool-use",
            "LLMOps",
            "Evaluation",
            "AI Harness"
          ],
          "featured": true,
          "url": "/zh/work/enterprise-agent-platform"
        },
        {
          "slug": "ai-data-model-platform",
          "title": "AI 数据标注与模型服务平台",
          "summary": "自主创业阶段从 0 到 1 交付多模态数据标注与模型服务平台，打通训练数据、模型产物、自动标注、质量校验和 Serverless 推理链路。",
          "tags": [
            "AI Data Platform",
            "Model Serving",
            "Serverless",
            "Multimodal",
            "Full-stack"
          ],
          "featured": true,
          "url": "/zh/work/ai-data-model-platform"
        },
        {
          "slug": "aigc-saas-platform",
          "title": "AIGC SaaS 与模型服务平台",
          "summary": "负责生成式 AI SaaS 的 Web 工程、微服务拆分、模型服务调用链和平台化交付，参与产品从 0 到 1 建设。",
          "tags": [
            "AIGC SaaS",
            "Model Serving",
            "Stable Diffusion",
            "Microservices",
            "DevOps"
          ],
          "featured": true,
          "url": "/zh/work/aigc-saas-platform"
        },
        {
          "slug": "commerce-platform-engineering",
          "title": "阿里高流量商业化与直播工程",
          "summary": "负责阿里淘宝与本地生活高流量跨端 Web、微前端、广告归因、日志 SDK、JsBridge 和性能优化。",
          "tags": [
            "Micro-frontend",
            "SSR",
            "Attribution",
            "Performance",
            "SDK"
          ],
          "featured": true,
          "url": "/zh/work/commerce-platform-engineering"
        }
      ],
      "lab": [
        {
          "slug": "dpo-tool-use",
          "title": "DPO for Tool-use Preference",
          "summary": "将工具调用问题样本转化为偏好数据，连接回放评测、策略优化与模型改进。",
          "tags": [
            "DPO",
            "Tool-use",
            "Preference Data"
          ],
          "featured": false,
          "url": "/zh/lab/dpo-tool-use"
        },
        {
          "slug": "eval-harness",
          "title": "Agent 回放评测框架",
          "summary": "用生产 trace 构建可复现的 Agent 回归评测，支撑 prompt、工具和模型变更。",
          "tags": [
            "Evaluation",
            "Replay",
            "Regression"
          ],
          "featured": true,
          "url": "/zh/lab/eval-harness"
        }
      ],
      "writing": [
        {
          "slug": "agent-platform-positioning-assessment",
          "title": "企业级 Agent 平台能力地图",
          "summary": "面向生产级 Agent 平台的 runtime、工具治理、评测回放、团队工程和业务价值框架。",
          "tags": [
            "Agent Platform",
            "Architecture",
            "Operating Model"
          ],
          "featured": true,
          "url": "/zh/writing/agent-platform-positioning-assessment"
        },
        {
          "slug": "agent-evaluation-playbook",
          "title": "企业 Agent 评测手册",
          "summary": "把 Agent 评测从主观试用变成可回放、可分层、可发布门禁的工程系统。",
          "tags": [
            "Evaluation",
            "LLMOps",
            "Agent"
          ],
          "featured": true,
          "url": "/zh/writing/agent-evaluation-playbook"
        }
      ]
    },
    "en": {
      "cases": [
        {
          "slug": "posttraining-data-flywheel",
          "title": "Agent Platform Data Feedback System",
          "summary": "Connects production traces, issue cases, evaluation samples, and preference data so online behavior becomes a continuous quality improvement mechanism.",
          "tags": [
            "Preference Data",
            "DPO",
            "Evaluation"
          ],
          "featured": false,
          "url": "/en/work/posttraining-data-flywheel"
        },
        {
          "slug": "enterprise-agent-platform",
          "title": "Production-grade Agent Platform Architecture",
          "summary": "Owned a cross-industry AI Agent customer-service SaaS platform from architecture evolution through production operations and expanded the team AI development harness across 40 repositories.",
          "tags": [
            "Agent Runtime",
            "Tool-use",
            "LLMOps",
            "Evaluation",
            "AI Harness"
          ],
          "featured": true,
          "url": "/en/work/enterprise-agent-platform"
        },
        {
          "slug": "ai-data-model-platform",
          "title": "AI Data Labeling and Model-serving Platform",
          "summary": "Built and delivered a multimodal data-labeling and model-serving platform from 0 to 1, connecting training data, model artifacts, auto-labeling, quality validation, and serverless inference.",
          "tags": [
            "AI Data Platform",
            "Model Serving",
            "Serverless",
            "Multimodal",
            "Full-stack"
          ],
          "featured": true,
          "url": "/en/work/ai-data-model-platform"
        },
        {
          "slug": "aigc-saas-platform",
          "title": "AIGC SaaS and Model-serving Platform",
          "summary": "Owned web engineering, microservice decomposition, model-serving call chains, and platform operations for 0-to-1 generative AI SaaS delivery.",
          "tags": [
            "AIGC SaaS",
            "Model Serving",
            "Stable Diffusion",
            "Microservices",
            "DevOps"
          ],
          "featured": true,
          "url": "/en/work/aigc-saas-platform"
        },
        {
          "slug": "commerce-platform-engineering",
          "title": "Alibaba High-traffic Commerce and Live Engineering",
          "summary": "Owned high-traffic cross-platform web engineering, micro-frontends, ad attribution, logging SDKs, JsBridge, and performance optimization across Alibaba Taobao and Local Services.",
          "tags": [
            "Micro-frontend",
            "SSR",
            "Attribution",
            "Performance",
            "SDK"
          ],
          "featured": true,
          "url": "/en/work/commerce-platform-engineering"
        }
      ],
      "lab": [
        {
          "slug": "dpo-tool-use",
          "title": "DPO for Tool-use Preference",
          "summary": "Turns tool-use issue cases into preference data connecting replay evaluation, policy optimization, and model improvement.",
          "tags": [
            "DPO",
            "Tool-use",
            "Preference Data"
          ],
          "featured": false,
          "url": "/en/lab/dpo-tool-use"
        },
        {
          "slug": "eval-harness",
          "title": "Agent Replay Evaluation Harness",
          "summary": "Built reproducible Agent regression evaluation from production traces for prompt, tool, and model changes.",
          "tags": [
            "Evaluation",
            "Replay",
            "Regression"
          ],
          "featured": true,
          "url": "/en/lab/eval-harness"
        }
      ],
      "writing": [
        {
          "slug": "agent-platform-positioning-assessment",
          "title": "Enterprise Agent Platform Capability Map",
          "summary": "A production Agent platform framework across runtime, tool governance, replay evaluation, team engineering, and business value.",
          "tags": [
            "Agent Platform",
            "Architecture",
            "Operating Model"
          ],
          "featured": true,
          "url": "/en/writing/agent-platform-positioning-assessment"
        },
        {
          "slug": "agent-evaluation-playbook",
          "title": "Enterprise Agent Evaluation Playbook",
          "summary": "Turning Agent evaluation from subjective trial into replayable, layered, release-gating engineering.",
          "tags": [
            "Evaluation",
            "LLMOps",
            "Agent"
          ],
          "featured": true,
          "url": "/en/writing/agent-evaluation-playbook"
        }
      ]
    }
  }
}