When products iterate rapidly, developer ecosystems expand, and AI enters engineering workflows, content systems for software and platform products must evolve.
在软件与平台产品领域，技术内容不再只是帮助文档，而是直接影响开发者上手效率、知识流转质量、产品运营效率与用户满意度。
In software and platform products, technical content is no longer just documentation — it directly affects developer onboarding, knowledge flow, product operations, and user satisfaction.
但在很多企业中，技术内容体系仍停留在“文档层面”。
But in most companies, technical content systems still remain at the level of manuals.
你是否正在面对这些真实问题？
This is not just a documentation problem. It is a challenge of knowledge flow, developer experience, and engineering efficiency.
在软件与平台产品中，技术内容正在直接影响：
Why technical content is harder in software and platform products
Leading companies are doing this.
Professionals from software and platform product companies are already joining tcworld China 2026
以下企业的专业人士已经参与 tcworld China 2026：
Professionals from the following companies are already joining tcworld China 2026:
这些企业共同面对的挑战是：产品迭代快、知识分散、工程链条长，内容必须同时支撑开发者体验、知识运营与系统协同。
These companies share similar challenges: rapid product iteration, fragmented knowledge, long engineering chains, and the need for content that supports developer experience, knowledge operations, and system collaboration.
Selected sessions relevant to software and platform products
Building an AI-Driven Knowledge Flywheel to Empower Cloud Service Efficiency
By 苏晓萌 信息体验工程师，华为云计算技术有限公司 / Information Experience Engineer, Huawei Cloud Computing Technologies Co., Ltd.
该议题围绕“用户声音洞察—知识生产—知识沉淀—场景应用”的全流程，构建 AI 驱动的知识飞轮体系。
通过标准化需求管理、分类分级流程与 AI 智能检索平台，知识资产得以系统化沉淀与自动化流转。
对于云服务、平台产品和支持体系复杂的软件企业，这一议题直接对应知识管理断链与服务效率问题。
This session focuses on building an AI-driven knowledge flywheel across the full chain of user insight, knowledge production, knowledge accumulation, and scenario application.
Through standardized workflows and AI-assisted retrieval, knowledge assets can be systematically accumulated and circulated automatically.
It is highly relevant for cloud services, platform products, and software organizations dealing with fragmented knowledge and service inefficiency.
Multi-Agent Technical Documentation Solution: AI Empowering the Entire Documentation Lifecycle
By 汪鸿儒 产品运营经理，平安科技 / Product Operations Manager, Ping An Technology
面对复杂产品文档的同步、审核、校对和运营维护难题，
该议题提出一套基于 LLM、语义检索和智能体技术的多智能体解决方案，
推动技术文档从被动固化的资产转变为可自动审校、持续演进的“主动”资源。
To address documentation synchronization, review, proofreading, and maintenance challenges for complex products,
this session presents a multi-agent solution based on LLMs, semantic retrieval, and agent technology,
transforming documentation from passive static assets into active resources that can review themselves and continuously evolve.
A Writing Engineering Practice for Agent Skill Invocation and Stable Execution Architecture Based on Structured Databases
By 张莹 高级技术内容工程师，蚂蚁集团 / Senior Technical Content Engineer, Ant Group
该议题聚焦多 Agent 应用开发中的稳定性问题，
提出将 skill、命令与交互逻辑存储于结构化数据库，并通过数据库查询与操作语法驱动 Agent 执行，
从而提升复杂任务中的鲁棒性、可追溯性与可扩展性。
This session focuses on stability challenges in multi-agent application development,
proposing an architecture where agent skills, commands, and interaction logic are stored in structured databases and executed through database-driven interactions,
thereby improving robustness, traceability, and scalability for complex writing tasks.
Developing Developer-Centric Open-Source Docs: AI-Powered Collaborative Information Building
By 樊雅清 信息体验工程师，华为 / Information Experience Engineer, Huawei
在开源生态和开发者共建背景下，
该议题展示如何通过文档系统设计、写作机制、开放协作与 AI 生成/质检/门禁能力，
提升开发者文档体系的质量与效率。
对于 API、SDK、开源项目和平台能力建设尤具参考价值。
In the context of open ecosystems and developer co-creation,
this session shows how documentation system design, writing mechanisms, open collaboration, and AI-enabled generation, quality checks, and gates
can improve the quality and efficiency of developer documentation systems.
It is especially relevant for APIs, SDKs, open-source projects, and platform capability documentation.
Developing and Integrating AI Agents into the Engineering Workflows
By Anna Goncharova 文档工程师，inDrive / Documentation Engineer, inDrive
该议题聚焦如何将 AI 智能体直接集成到软件开发生命周期中，
使其与版本控制系统联动、响应代码变更，并通过 GitHub Actions 等方式持续输出可靠内容。
它为工程团队提供了一条将文档智能体融入现有流程、而非增加额外负担的实践路径。
This session focuses on integrating AI agents directly into the software development lifecycle,
allowing them to interact with version control, respond to code changes, and continuously deliver reliable outputs through GitHub Actions and similar mechanisms.
It provides a practical path for engineering teams to embed documentation agents into existing workflows without creating extra burden.
From Manual to Automated: A Practical Introduction to the Model Context Protocol for Technical Writers
By Amandeep Singh Talwar 内容设计经理，Autodesk / Content Design Manager, Autodesk
该议题通过“自动生成发布说明”的真实案例，
介绍模型上下文协议（MCP）如何帮助技术文档工程师将 AI 助手连接到企业工具，
自动提取信息、同步内容并减少重复性工作。
对于希望从局部自动化迈向系统性自动化的软件团队，这是一条非常实用的入门路径。
Using a real-world release notes automation example,
this session introduces how the Model Context Protocol (MCP) helps technical documentation engineers connect AI assistants with enterprise tools,
automate information retrieval, synchronize content, and reduce repetitive work.
It is a practical entry point for software teams moving from isolated automation to systematic automation.
Humanizing Technical Information: Smart Strategies for Error Code Content Management
By 潘玲 技术文档经理，上海云轴科技股份有限公司 / Technical Documentation Manager, ZStack
错误信息是软件与用户沟通的重要触点，
该议题提出一套覆盖错误信息设计、智能生产、本地化与高效交付的系统化方法论，
帮助团队将分散、难懂、难翻译的错误信息转变为用户可理解、可操作、可多语言交付的内容体系。
Error messages are a critical touchpoint between software and users.
This session presents a systematic method for error content design, intelligent production, localization, and efficient delivery,
helping teams transform fragmented and hard-to-understand error information into user-friendly, actionable, and multilingual content systems.
→ 查看完整议程 / Explore the full program
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