Enterprise Intelligent Agents with LLMs

The book begins by explaining how AI is shifting from simple text prediction to autonomous, reasoning-driven systems that can plan, act, and learn. Readers will understand the principles behind agentic architectures, the distinction between LLM-based apps and true AI agents, and how modern frameworks enable memory, tool use, and collaboration. This section establishes the foundation for designing intelligent, goal-oriented agents capable of operating within complex enterprise ecosystems. Further, the book transitions from theory to hands-on practice, showcasing frameworks such as LangChain, AutoGen, CrewAI, LangGraph, and Semantic Kernel. Each framework is explored through practical examples, code walkthroughs, and architectural insights that help readers build and deploy scalable agent systems in real-world settings. The final section explores how agentic AI is transforming industries automating DevOps, enhancing customer support, and powering decision intelligence while addressing key challenges in security, governance, and ethical AI design. Complete with benchmarking guidance and future trends, this book is an indispensable guide for architects, developers, and enterprise leaders looking to operationalize AI agents at scale. You Will: Learn how agentic AI differs from traditional LLM/SLM systems and why it matters Understand step-by-step implementation of agent frameworks like LangChain, AutoGen, and CrewAI Practise how to deploy agents in cloud-native, serverless, or hybrid environments master methods for evaluating, securing, and governing autonomous AI systems This book is for : Data scientists, technical product managers and AI strategists exploring enterprise automation.