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When software was shipped in the early days of computing, it was shipped with thick printed manuals. If you need any help with software, you need to physically locate the right index in the printed manual to find the relevant topic and page number to read the content. Once the software was shipped on a compact disc (CD), the documentation was delivered digitally. Documentation was part of the software, and once you clicked the help icon, you could search for content and get help. In the SaaS era, documentation is provided as an in-app resource to help users within the product, and often, your customers visit your documentation site to browse information. Until now, documentation has been written for humans to learn and troubleshoot software issues.
Many modern documentation sites already have chatbots. Your knowledge base contents are chunked and retrieved based on your customer’s prompts. Chatbot’s response accuracy depends on content quality; thus, technical writers are structuring content to be AI-friendly. However, the new audience for the documentation site is an AI agent that reads your documentation and acts based on its inference. The AI agent accomplishes tasks on your behalf autonomously using your knowledge base content. Thus, it is becoming important to write content that is comprehensible by both humans and AI agents.
From One Audience to Two: What Changed
Human readers dislike repetition; thus, technical writers use tables, media artefacts, animated GIFs, and videos to improve the comprehensibility and engagement of their content.
Many companies report that AI agents are visiting and reading their content more than human visitors do. AI bots drove about 2% of all web traffic (≈ 1 in 50 visits), up from about 0.5% in early 2025. This indicates the bot’s share quadrupled over the year.
Thus, it is important for technical writers to understand that their audience is changing, and that modern customers are more interested in using AI agents than in reading docs to accomplish tasks themselves.
How Humans and AI Read Documentation Differently
There is a significant difference between how human and AI agents consume documentation.
Humans have shorter attention spans; thus, they skim content for relevance. Humans read non-linearly, jumping to different sections of their knowledge base. Sometimes humans infer meaning from their own experience and context that aligns with the article’s content. Humans also respond to tone and inclusive terms used in the content. Humans also exhibit emotions while reading documentation and empathize. Humans must read a lot of content for clarification if any ambiguity is present in the content. Humans use good judgment based on their moral values and principles to take the right set of actions. In general, humans read content to interpret. Then they execute a plan to accomplish a task. If any information is missing from the content, they fill those gaps with intuition.
Because of their unlimited attention span and large context window, AI agents can consume large amounts of content. Even if the content cannot be held in their context window, it can be chunked for AI agents. Since AI agents can access large amounts of content via the Model Context Protocol (MCP), they can easily gain a holistic perspective on it and have thorough knowledge of its context. More importantly, AI agents can accomplish workflows in a few minutes. In general, AI agents plan a set of activities based on the content and can automatically execute them. If any ambiguity is present in the content, then it makes it harder for the AI agent to plan. It may require human assistance if any content contains ambiguity.
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What Documentation Must Include for AI Agents
There are many aspects of content to consider when writing for an AI agent. This includes
- Content structure,
- Explanation of each section in detail,
- Practical scenarios in terms of use cases.
- Explicit definitions & terminology normalization
The content scope should be tightly defined and adhered to. If the AI agents should call external tools, the description of those calls and other capabilities should be clearly documented.
If AI agents are designed to troubleshoot issues, the troubleshooting guide should cover all scenarios and error codes and provide detailed instructions for fixing them. Any limitations and constraints should be clearly stated so that AI agents can understand the content thoroughly. Having clear API documentation helps with tool calling by AI agents.
How TOON Improves AI Understanding
Token-Oriented Object Notation (TOON) is a data format that is optimized for data transfer between Large Language Models (LLMs). This data format can be used to supply content to AI agents, enabling cost optimization. Instead of supplying content in JSON or Markdown, TOON uses a token-based format that is easily understood by the LLM underpinning the AI agent. This TOON format helps your AI agent in
- Knowledge retrieval that involves Retrieval Augmented Generation (RAG) to fit more content in the context window
- Tool calling that involves high-volume API calls that transfer large chunks of content.
- A consistent data structure that makes it easy for AI agents to transfer content with each other
Conclusion: Documentation as Infrastructure
Writing content that is comprehensible to both humans and AI agents is becoming the norm for documentation. Documentation content should never be stable; it should be continuously updated so that AI agents can use it to plan actions to accomplish tasks. Clear documentation not only empowers human readers but also guides AI agents’ behaviors. The technical writing team must invest in a robust content structure, produce information at a granular level, and supplement metadata. This helps future-proof your documentation and keep it AI-agent-ready.
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