As AI agents become primary consumers of documentation, the battle for superior developer experience shifts from visual design to content accuracy. An agent reading raw markdown doesn't care about UI, making the underlying information paramount and the foundation of a modern DevEx strategy.

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 Now, with AI support agents relying on this content as their source of truth, outdated information leads to immediate, tangible failures.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.

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.

Enterprises are trapped by decades of undocumented code. Rather than ripping and replacing, agentic AI can analyze and understand these complex systems. This enables redesign from the inside out and modernizes the core of the business, bridging the gap between business and IT.

Experienced engineers using tools like Claude Code are no longer writing significant amounts of code. Their primary role shifts to designing systems, defining tasks, and managing a team of AI agents that perform the actual implementation, fundamentally changing the software development workflow.

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.

As AI rapidly generates code, the challenge shifts from writing code to comprehending and maintaining it. New tools like Google's Code Wiki are emerging to address this "understanding gap," providing continuously updated documentation to keep pace with AI-generated software and prevent unmanageable complexity.

As AI generates more code, the core engineering task evolves from writing to reviewing. Developers will spend significantly more time evaluating AI-generated code for correctness, style, and reliability, fundamentally changing daily workflows and skill requirements.