Mastering Agentic AI
Advanced TechniquesBy Anand Vemula
Length1h 36m
About this audiobook
Mastering Agentic AI: Advanced Techniques delves into the cutting-edge methodologies for designing, developing, and deploying autonomous AI agents capable of self-improvement, decision-making, and adaptive learning. This book provides a deep exploration of agentic AI, distinguishing it from traditional AI systems by emphasizing autonomy, goal-driven behavior, and self-directed learning.
The book covers key architectural principles, including cognitive models, reinforcement learning, and multi-agent collaboration. It explores frameworks such as OpenAI Gym, TensorFlow Agents, and LangChain, equipping readers with the tools to build intelligent AI systems. Practical implementation strategies are discussed, including optimizing agentic behavior for real-world applications in business automation, healthcare, finance, and cybersecurity.
Advanced topics such as ethical considerations, safety mechanisms, and explainability in agentic AI are addressed to ensure responsible AI development. The book also covers integration with large language models (LLMs) and retrieval-augmented generation (RAG) systems to enhance decision-making capabilities.
Through case studies, best practices, and future trends, Mastering Agentic AI: Advanced Techniques serves as an essential guide for AI researchers, engineers, and business leaders aiming to harness the power of autonomous AI agents. Whether developing self-learning systems or optimizing agentic AI for enterprise solutions, this book provides a comprehensive roadmap for mastering next-generation AI technologies.
Audiobook details
GenreEducation and Learning, Self-Help
Length1 hr 36 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateMay 21, 2025
LanguageEnglish
Table of contents
1Defining Agentic AI and Its Core Characteristics
2Perception, Reasoning, and Decision-Making
3Symbolic AI vs. Connectionist AI
4Goal-Oriented and Task-Specific Agents
5Policy-Based and Value-Based Learning Methods
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6Short-Term vs. Long-Term Memory in AI Systems
7Agentic AI in Conversational and Assistive AI Systems
8Agent Coordination and Communication Strategies
9Case Studies in Multi-Agent AI Systems
10Bias and Fairness in Agentic AI
11The Future of Self-Improving AI Systems
12The Roadmap to General Artificial Intelligence (AGI)
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