Architecting for AI: What Changes in Software Design and What Still Holds

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A developer-authored book argues that AI introduces a probabilistic, costly, and unaccountable component into systems designed before it existed. The author contends that the industry's business model, development methods, and architecture must all adapt, while foundational practices like capability maps, domain modeling, and documented decisions remain essential. The book offers practical guidance for extending enterprise architecture frameworks to cover AI, including evaluation-based testing, guardrails for agents, and risk-tiered trust design. Each chapter addresses a common belief about AI in software, examines what is genuinely valuable about it, and closes with a checklist of recommended practices. The author aims the book at solution and enterprise architects, tech leads, and engineering managers, as well as developers concerned about their roles.

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