As AI agents become the primary 'users' of software, design priorities must change. Optimization will move away from visual hierarchy for human eyes and toward structured, machine-legible systems that agents can reliably interpret and operate, making function more important than form.

To get superior results from AI coding agents, treat them like human developers by providing a detailed plan. Creating a Product Requirements Document (PRD) upfront leads to a more focused and accurate MVP, saving significant time on debugging and revisions later on.

AI prototyping shifts the purpose of a design system from a human-centric resource, reinforced through culture and reviews, to a machine-readable memory bank. The primary function becomes documenting rules and components in a way that provides a persistent, queryable knowledge base for an AI agent to access at all times.

For decades, keeping documentation updated was a low-priority task. Now, with AI support agents relying on this content as their source of truth, outdated information leads to immediate, tangible failures. This creates the urgent business case to finally solve knowledge decay.

As AI agents handle the mechanics of code generation, the primary role of a developer is elevated. The new bottlenecks are not typing speed or syntax, but higher-level cognitive tasks: deciding what to build, designing system architecture, and curating the AI's work.

Moving PRDs and other product artifacts from Confluence or Notion directly into the codebase's repository gives AI coding assistants persistent, local context. This adjacency means the AI doesn't need external tool access (like an MCP) to understand the 'why' behind the code, leading to better suggestions and iterations.

Documentation is shifting from a passive reference for humans to an active, queryable context for AI agents. Well-structured docs on internal APIs and class hierarchies become crucial for agent performance, reducing inefficient and slow context window stuffing for faster code generation.

In the era of zero-click AI answers, the goal shifts from maximizing time-on-page to providing the shortest path to a solution. Content must lead with a direct, data-dense summary for AI agents to easily scrape and cite.

Documentation is no longer just for humans. AI agents now read it directly as operational input, making its accuracy critical for system function. Outdated docs, once a nuisance, now cause system failures, elevating documentation to the level of essential infrastructure.