Enterprise Guide for Implementing Generative AI and Agentic AI

Generative AI and Agentic AI together are revolutionizing the technology landscape, with profound and far-reaching impacts across industries. Organizations are increasingly adopting these technologies to drive innovation, enhance unstructured content management, and improve problem-solving capabilities. With Agentic AI, enterprises are moving towards the development of intelligent systems that can plan, reason, and act with autonomy. While early proof-of-concepts (POCs) demonstrated the potential of these technologies, the current shift is toward responsible and scalable production implementations that leverage both generative and agentic capabilities.
This book begins by guiding you through the technological evolution of AI, from early machine learning to today's large language models (LLMs) and agentic systems. It then explores a wide range of use cases across industries, highlighting how LLMs can support decision-making, and how Agentic AI enables dynamic, collaborative systems that act with autonomy and intent. This is followed by Design Patterns across the lifecycle of AI solution development, deployment and monitoring. Readers will then gain insights into the methodologies for developing and deploying Generative and Agentic AI solutions at an enterprise level. A featured implementation demonstrates how Agentic AI can be effectively put into action.
The book also introduces essential concepts such as MLOps, LLMOps, and Responsible AI principles which are critical for transitioning the AI solutions from experimentation to production. These principles ensure that AI deployments are scalable, secure, ethical and compliant. The book concludes with key takeaways and best practices for developing, evaluating, deploying and scaling AI applications responsibly and effectively within enterprise settings.

You Will:

• Understand key design patterns to develop, deploy and monitor a Generative AI solution effectively.
• Learn how to develop and implement a production-ready Agentic AI use case.
• Discover best practices for building scalable, secure, and enterprise-grade AI solutions.
• Understand how to assess and mitigate risks using Responsible AI principles and LLMOps best practices.

This book is for: Enterprise Software Engineers and Architects

Rahul Bhattacharya is a thought leader, consultant and an active speaker in AI & ML. In his current role as the AI leader of a global firm, he is responsible for working with stakeholders across globe to drive transformative AI programs for clients across industries and functions. With over 30 years of diverse experience in implementing real-world business applications using AI and machine learning globally, Rahul is a trusted advisor and mentor in his field. Rahul is a Birla Institute of Technology and Science (BITS), Pilani graduate and is passionate about learning and teaching. He defines himself as a curious traveler, consummate foodie and lifelong objectivist. A student of global cultures, he has an eclectic taste in literature, music and movies. He is a husband and a father of two Gen-Z boys.

Shakuntala Gupta is a thought leader, consultant in the areas of AI, ML, NLP, Big Data Analytics, product development. In her current role as the AI and Data leader she helps deliver transformative AI programs for clients across various industries and functions. With over two decades of diverse experience in implementing advanced analytics solutions for real-world business challenges using AI, Data and machine learning, Shakuntala is a true technology enthusiast. With a master's degree in computer science, she defines herself as a continuous learner. Outside of her technical work she loves exploring new cuisines, indulges herself in fiction books and cherish her time at beach whenever possible.

Vikas Sinha is a Machine Learning Engineer, Architect and a thought leader in AI & ML. As organizations struggles to scale AI and realize business impact, his role is to conceptualize, build and deploy AI, ML @scale for Clients across sectors globally working with client enterprise and data architects, CDOs and Businesses. Vikas has expertise across cloud platforms and have developed end to end Responsible MLOps, LLMOps architectures on Azure, AWS and GCP enabling clients to productionize multiple AI use cases. He has also led innovations in client engagements through implementation of novel research in multi-modal AI, simplified AI, and enabling operationalization of complex AI architecture as part of enterprise solution. He also mentors' college graduates in the field of Data Science, AI/ML/GenAI/MLOps/LLMOps and have delivered guest lectures at reputed Indian Universities. He is father to a beautiful angel.