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Agentic Software Engineering presents a rigorous engineering framework for designing, building, testing, securing, and governing software systems in which AI agents participate directly in the development lifecycle. As software agents move beyond code completion and begin planning tasks, modifying repositories, running tools, coordinating with other agents, testing their own work, and operating across long-running workflows, traditional software engineering must evolve. The central challenge is no longer simply generating better code. It is building an engineering system in which autonomous and semi-autonomous agents can act within explicit boundaries, produce verifiable evidence, and remain accountable to human intent. This book develops agentic software engineering as a complete discipline. It examines how specifications, architecture, context, memory, execution environments, tools, permissions, verification systems, security controls, approval boundaries, and observability must work together to support reliable AI-native development. Topics include: A multi-chapter Atlas case study connects the concepts from specification and architecture through implementation, verification, security, and production operation. Rather than focusing on temporary product features or vendor-specific prompting techniques, Agentic Software Engineering emphasizes durable engineering principles that remain useful as models, tools, and platforms change. Written for experienced software engineers, architects, technical leaders, AI platform engineers, DevOps and security professionals, this book provides a systematic foundation for organizations preparing to move from AI-assisted programming toward controlled, production-grade agentic engineering.
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