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Layered Volatility Decomposition: Foundation for AI-Driven Development
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Software architecture is fundamentally about managing change. Layered Volatility Decomposition (LVD) bridges the gap between classical engineering disciplines-like Juval Löwy's Volatility-Based Decomposition (VBD) and Clean Architecture-and the demands of modern AI-integrated software engineering. This technical text establishes a formal, graph-theoretic framework for structural decomposition based on rates of change rather than business domains. Covering everything from Model Context Protocol (MCP) integrations to building predictable, multi-agent AI coding ecosystems, this book provides software architects, principal engineers, and system designers with the mathematical and practical blueprints needed to survive and scale in the era of AI-driven development.
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Software architecture is fundamentally about managing change. Layered Volatility Decomposition (LVD) bridges the gap between classical engineering disciplines-like Juval Löwy's Volatility-Based Decomposition (VBD) and Clean Architecture-and the demands of modern AI-integrated software engineering. This technical text establishes a formal, graph-theoretic framework for structural decomposition based on rates of change rather than business domains. Covering everything from Model Context Protocol (MCP) integrations to building predictable, multi-agent AI coding ecosystems, this book provides software architects, principal engineers, and system designers with the mathematical and practical blueprints needed to survive and scale in the era of AI-driven development.
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