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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

Introspection in intelligent agents

Introspection refers to the process by which an intelligent agent examines and analyzes its own cognitive processes, decisions, and behaviors. This capability allows agents to gain deeper insights into their actions, identify patterns, and adjust their strategies based on reflection. Introspection is essential in advancing intelligent agents from simple task performers to systems that can continually evolve and improve over time, similar to the way humans reflect on past experiences to make better future decisions.

In agent-based systems, introspection plays a crucial role in enhancing performance and adaptability. When agents introspect, they evaluate their reasoning and decision-making pathways, allowing them to detect any flaws, biases, or inefficiencies in their processes. This leads to a more refined understanding of the environment and their own functioning, enabling them to make more informed choices and adapt their behavior. For example...

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Building Agentic AI Systems
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