Software Documentation

With the rise of AI, can technical writing naturally achieve 10x or even 100x efficiency gains? The answer is no—multiple challenges still constrain the effective use of AI. Today, technical documentation management in large enterprises commonly faces three major pain points: misalignment between R&D and documentation leading to version chaos, where operations teams often use old manuals to operate new equipment; static documents that fail to meet the needs of different roles, resulting in low information retrieval efficiency; and high collaboration costs across the full lifecycle, with repetitive work accounting for over 60%, trapping technical writers in low-efficiency internal competition.

Based on real-world projects, this session presents a human–AI collaborative practice that builds a documentation development model centered on intelligent information blocks. The approach achieves a 60% reduction in business process complexity and supports a flatter organizational structure, offering a benchmark “living documentation” practice that intelligently bridges the gap from content to action.

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.