practice in 3 steps Build iteratively your EA codex and shift progressively to architecture as code Extend EA framework • Extend the EA metamodel: Add AI agents, models, data products, and MCP servers to your architecture repository so AI becomes a first-class element of the landscape. • Define governance guardrails: Specify authority levels, policies, and risk classes to control how agents can act across systems and data. • Link architecture layers: Map capabilities to agents, data products, and platforms to make architecture executable, not just descriptive. • Align with regulations: Create regulation controls and risks and mapped them to your framework 1 Capitalize knowledge • Build an EA playbook: Formalize patterns, specs, and decision frameworks to guide AI-driven architecture consistently. • Turn knowledge into skills: Convert principles, standards, and review checklists into reusable AI skills for automated analysis. • Structure architecture data: Maintain LeanIX Fact Sheets and metadata as machine-readable context for agents. • Manage value explicitly: Maintain portfolios with value hypotheses, KPIs, and risk classes to steer AI initiatives. 2 Create intelligence • Create AI skills: Develop reusable capabilities for governance checks, architecture analysis, and design generation. • Deploy agent teams: Introduce orchestrator agents and domain sub- agents to support EA workflows and decision-making. • Expose architecture context: Use MCP servers and controlled APIs to provide secure, governed knowledge to AI agents. • Adopt progressive autonomy: Start with human-in-the-loop supervision and evolve toward autonomous execution as maturity grows. 3