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Building Agentic AI Systems

Building Agentic AI Systems

By : Anjanava Biswas, Wrick Talukdar
4 (1)
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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

Answers

  1. Key characteristics of intelligent agents include reactivity, proactiveness, social ability, autonomy, and the capability to learn and adapt.
  2. The main architectural patterns for intelligent agents are deliberative (knowledge-based), reactive (behavior-based), and hybrid architectures.
  3. Deliberative architectures excel in complex reasoning and planning but may struggle with real-time responsiveness in dynamic environments. Reactive architectures are well suited for dynamic environments requiring rapid responses but lack long-term planning and abstract reasoning capabilities.
  4. A multi-agent system (MAS) consists of multiple intelligent agents that interact, cooperate, and coordinate to achieve collective goals. Key characteristics include autonomy, interaction, adaptability, distributed control, scalability, heterogeneity, and decentralized data and knowledge.
  5. The main interaction mechanisms in MASs are cooperation (working towards a common goal), coordination...
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Building Agentic AI Systems
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