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Subrat SaurabhAuthor of Kuch Woh PalBhuvaneswari Subramani is a visionary technology leader specializing in cloud engineering, AI-driven transformation, and modernization. With over two decades of experience across multi-cloud architectures, she guides engineering teams in agentic AI, generative AI, intelligent automation, and cloud-native platform design. Recognized as an AWS Hero, AWS Ambassador, and Google Women Techmakers Ambassador, she combines deep technical expertise with a strong focus on clarity, quality, and business value. In 2025, Bhuvana led the championship-winning team at the Road to re:Invent Hackathon, a globalRead More...
Bhuvaneswari Subramani is a visionary technology leader specializing in cloud engineering, AI-driven transformation, and modernization. With over two decades of experience across multi-cloud architectures, she guides engineering teams in agentic AI, generative AI, intelligent automation, and cloud-native platform design.
Recognized as an AWS Hero, AWS Ambassador, and Google Women Techmakers Ambassador, she combines deep technical expertise with a strong focus on clarity, quality, and business value. In 2025, Bhuvana led the championship-winning team at the Road to re:Invent Hackathon, a global high-stakes innovation challenge. Her work spans architecture, strategy, product thinking, and hands-on engineering.
Bhuvana is also a respected blogger and speaker who simplifies complex ideas for teams adopting AI-native development.
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Artificial intelligence (AI) is reshaping everyday development work. AI-Driven Development Lifecycle (AI-DLC), invented and open-sourced by Amazon Web Services (AWS) in 2025, is a methodology in which AI takes part in each phase of the build while humans shape direction, review outcomes, and guide quality. Traditional SDLC depends on staged handoffs while AI-DLC brings humans and AI into one continuous flow.
This book explains that shift in a simple a
Artificial intelligence (AI) is reshaping everyday development work. AI-Driven Development Lifecycle (AI-DLC), invented and open-sourced by Amazon Web Services (AWS) in 2025, is a methodology in which AI takes part in each phase of the build while humans shape direction, review outcomes, and guide quality. Traditional SDLC depends on staged handoffs while AI-DLC brings humans and AI into one continuous flow.
This book explains that shift in a simple and practical way. The AI-DLC approach shows how intent moves through stages and AI-Native rituals like Mob elaboration and mob construction for both brownfield and greenfield projects. Readers see how AI supports task decomposition, planning, code generation, refactoring, and validation in a way that reduces friction and improves flow. Two case studies outline how teams navigate legacy constraints and fresh builds, giving developers a close look at real-world decisions, prompt work, architecture choices, and review steps. The focus stays on easy adoption, predictable outcomes, and strong human judgment. Developers gain a grounded path to build reliable systems with speed, clarity, and confidence while staying fully in control of their work.
Artificial intelligence (AI) is reshaping everyday development work. AI-Driven Development Lifecycle (AI-DLC), invented and open-sourced by Amazon Web Services (AWS) in 2025, is a methodology in which AI takes part in each phase of the build while humans shape direction, review outcomes, and guide quality. Traditional SDLC depends on staged handoffs while AI-DLC brings humans and AI into one continuous flow.
This book explains that shift in a simple a
Artificial intelligence (AI) is reshaping everyday development work. AI-Driven Development Lifecycle (AI-DLC), invented and open-sourced by Amazon Web Services (AWS) in 2025, is a methodology in which AI takes part in each phase of the build while humans shape direction, review outcomes, and guide quality. Traditional SDLC depends on staged handoffs while AI-DLC brings humans and AI into one continuous flow.
This book explains that shift in a simple and practical way. The AI-DLC approach shows how intent moves through stages and AI-Native rituals like Mob elaboration and mob construction for both brownfield and greenfield projects. Readers see how AI supports task decomposition, planning, code generation, refactoring, and validation in a way that reduces friction and improves flow. Two case studies outline how teams navigate legacy constraints and fresh builds, giving developers a close look at real-world decisions, prompt work, architecture choices, and review steps. The focus stays on easy adoption, predictable outcomes, and strong human judgment. Developers gain a grounded path to build reliable systems with speed, clarity, and confidence while staying fully in control of their work.
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