Sairohith Thummarakoti – författare

The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.

Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows – all while meeting regulatory standards.

As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.

The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.

Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows – all while meeting regulatory standards.

As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.

Modern enterprises must address three concurrent challenges: rapid technological advancement, increasing regulatory requirements, and heightened customer expectations. In this context, the concept of cloud has evolved from a destination to an operating model. Pega Cloud 3 exemplifies this transformation. It provides a cloud-native execution environment, including pods for strict isolation, externalized stateful services for enhanced performance, Kubernetes for automated recovery and continuous delivery, and integrated low-code decisioning with governance. Pega Agentic AI on Cloud 3 synthesizes these components into a structured methodology, enabling agentic artificial intelligence (AI) that operates with evidence-based planning and defined safeguards. Key features of the book include:

The book is a practical guide focusing on actionable methodologies rather than theoretical concepts. Each architectural pattern supports incremental adoption, with all controls implemented programmatically, and every change is subject to audit and review. The book features a continuous case study, Northstar Enterprises, a diversified organization that adopted Cloud 3 to accelerate delivery, efficiently demonstrate compliance, and align scaling with measurable customer outcomes.

Modern enterprises must address three concurrent challenges: rapid technological advancement, increasing regulatory requirements, and heightened customer expectations. In this context, the concept of cloud has evolved from a destination to an operating model. Pega Cloud 3 exemplifies this transformation. It provides a cloud-native execution environment, including pods for strict isolation, externalized stateful services for enhanced performance, Kubernetes for automated recovery and continuous delivery, and integrated low-code decisioning with governance. Pega Agentic AI on Cloud 3 synthesizes these components into a structured methodology, enabling agentic artificial intelligence (AI) that operates with evidence-based planning and defined safeguards. Key features of the book include:

The book is a practical guide focusing on actionable methodologies rather than theoretical concepts. Each architectural pattern supports incremental adoption, with all controls implemented programmatically, and every change is subject to audit and review. The book features a continuous case study, Northstar Enterprises, a diversified organization that adopted Cloud 3 to accelerate delivery, efficiently demonstrate compliance, and align scaling with measurable customer outcomes.