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AI Augmented Engineering: Reimagining the Software Development Lifecycle with Agentic AI
AI-Augmented Engineering presents a practical, end-to-end guide to how Agentic AI is transforming modern software development across the entire Software Development Lifecycle (SDLC).
Covering every phase of the SDLC from requirements and architecture through development, testing, release, operations, governance, and measurement , this book explains how Agentic AI systems can reason across artifacts, maintain context over time, and support teams without undermining accountability. It introduces practical patterns for integrating AI into real workflows, including context-aware prompting, test intelligence, regression optimization, production feedback loops, and enterprise-safe orchestration using Model Context Protocol (MCP).
Written for engineering leaders, architects, quality professionals, and senior practitioners, AI-Augmented Engineering avoids vendor-specific guidance and short-lived tooling trends. Instead, it focuses on durable principles, operating models, and governance practices that enable organizations to scale AI responsibly while preserving trust, compliance, and human judgment.
This book serves as both a strategic reference and a practical playbook for teams seeking to move from experimental AI usage to disciplined, enterprise-ready AI-augmented software engineering.
Covering every phase of the SDLC from requirements and architecture through development, testing, release, operations, governance, and measurement , this book explains how Agentic AI systems can reason across artifacts, maintain context over time, and support teams without undermining accountability. It introduces practical patterns for integrating AI into real workflows, including context-aware prompting, test intelligence, regression optimization, production feedback loops, and enterprise-safe orchestration using Model Context Protocol (MCP).
Written for engineering leaders, architects, quality professionals, and senior practitioners, AI-Augmented Engineering avoids vendor-specific guidance and short-lived tooling trends. Instead, it focuses on durable principles, operating models, and governance practices that enable organizations to scale AI responsibly while preserving trust, compliance, and human judgment.
This book serves as both a strategic reference and a practical playbook for teams seeking to move from experimental AI usage to disciplined, enterprise-ready AI-augmented software engineering.
94 pages, Kindle Edition
Published January 1, 2026
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