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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
5
Part 2: Designing and Implementing Generative AI-Based Agents
10
Part 3: Trust, Safety, Ethics, and Applications

Understanding multi-agent systems

Multi-agent systems (MASs) represent an important subfield of the broader area of distributed artificial intelligence. They consist of several intelligent agents that interact, cooperate, and coordinate with each other to execute tasks and achieve collective goals. Each agent in a MAS is typically autonomous, capable of perceiving its environment through sensors, possessing a reasoning mechanism to make decisions, and acting upon those decisions to meet its design objectives. The collective behavior and interactions of these agents enable MASs to tackle complex problems that single-agent systems struggle with due to the inherent limitations of individual agents.

Examples of MASs can be found in various domains, demonstrating their applicability and effectiveness in solving complex problems:

  • Supply chain management and logistics: MASs can be used to optimize supply chain operations by coordinating the activities of different agents representing...
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Building Agentic AI Systems
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