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  • Book Overview & Buying Building Agentic AI Systems
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Building Agentic AI Systems

Building Agentic AI Systems

By : Anjanava Biswas, Wrick Talukdar
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Building Agentic AI Systems

Building Agentic AI Systems

4 (1)
By: Anjanava Biswas, Wrick Talukdar

Overview of this book

Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks. Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents. Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.
Table of Contents (18 chapters)
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Part 1: Foundations of Generative AI and Agentic Systems
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Part 2: Designing and Implementing Generative AI-Based Agents
10
Part 3: Trust, Safety, Ethics, and Applications

Use cases and examples

Reflective intelligent agents have been equipped to support a number of emerging business applications. A reflective agent can efficiently apply self-assessment and introspection to improve its performance against changing environments for the purpose of making more effective business decisions, thus continuing to improve in a transparent and explainable manner. Some examples of reflective agents applied in real business applications are as follows.

Customer service chatbots

Reflective customer service chatbots employ self-assessment methodologies to continuously improve their ability to provide effective and satisfactory responses to users. By introspecting on past conversations, these chatbots can identify patterns, strengths, weaknesses, and areas for improvement, enabling them to refine their knowledge base, response strategies, and overall interaction capabilities.

One key aspect of self-assessment is the ability to analyze the outcomes of past...

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Building Agentic AI Systems
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