May 21 – Page 2 – tcworld China

Exploring and Implementing AI Agents in Documentation Delivery

2026年2月10日 • 0 Comment

This presentation will introduce ZTE’s exploration and practice of integrating AI agents in documentation delivery. It will discuss how to build an AI agent system based on large language models, facilitating the transition of technical documentation from static knowledge to dynamic services. The presentation will focus on application scenarios such as intelligent Q&A, scenario-based content delivery, and automated knowledge maintenance. It demonstrates how the concept of “knowledge as a service” significantly improves document retrieval efficiency and operational support, providing practical insights for the industry’s shift toward intelligent documentation delivery.

From “Functionally Correct” to “User-Friendly”: Enhancing the Quality of Overseas Documentation through Differentiated Experience Design

2026年2月10日 • 0 Comment

Against the backdrop of internationalization, products intended for overseas markets commonly face three major challenges: long and complex support paths, high learning costs, and significant language barriers. As a critical component of the product, documentation encounters similar issues. User research shows that overseas users rely more heavily on documentation than domestic users, and the quality of documentation directly influences customer satisfaction with both the product and the brand.

Therefore, overseas documentation must go beyond accuracy and basic usability by improving readability and translatability, while establishing solid multilingual support. High-quality documentation experiences can better meet overseas user needs, support products in international markets, and enhance overall brand satisfaction.

In this presentation, we will demonstrate how information designers at Inovance break through global business challenges using a complete, practice-proven methodology—from user research to closed-loop validation. The practice includes differentiated design based on BUTP analysis, concept development, implementation, usability testing, and the establishment of a long-term feedback and iteration mechanism. This provides peers with a validated pathway for improving documentation quality, evolving from “ensuring accuracy” to “optimizing user experience.”

The Sovereign Editor: Local, Agentic Doc Reviews

2026年2月10日 • 0 Comment

As Generative AI evolves from passive chatbots to active agents, the role of the technical writer is shifting from content creator to information architect. This session introduces a cutting-edge, privacy-first workflow: the Automated Documentation Reviewer, built on the Model Context Protocol (MCP).

We will demonstrate a “Docs-as-Code” pipeline where an autonomous agent monitors a documentation repository, acting as a tireless preliminary editor. Unlike cloud-dependent tools that risk IP leakage, this workflow prioritizes Data Sovereignty. We will use various local models—such as DeepSeek R1 and other frontier labs’ models—to run locally, ensuring sensitive technical data never leaves the corporate firewall.

The architecture is designed for edge deployment with optional integration of larger models hosted on company servers through accessible API endpoints.

This session provides a guideline for enterprises seeking high-speed, compliant, and secure documentation engineering—eliminating reliance on third-party APIs while advancing technical writing standards.

A Writing Engineering Practice for Agent Skill Invocation and Stable Execution Architecture Based on Structured Databases

2026年2月10日 • 0 Comment

In multi-agent application development, traditional approaches that rely on text files (such as .md or .txt) to aggregate skills, commands, and interaction logic are often constrained by model limitations in quantization and parsing. This can lead to character loss or execution errors.

In this presentation, I will present a recently explored writing engineering approach: storing agent skills, commands, and interaction logic in structured databases, and driving agent interactions through database queries and operations. This approach enables a low-cost, highly stable execution environment. It ensures skill integrity and traceability, improves model robustness in complex task execution, and provides a scalable, engineering-oriented path for large-scale agent-based writing applications.

Page 2 of 2