Agentic AI: Enabling the Next Frontier of Autonomous Intelligence Systems

Description

Summary

This book, Agentic AI: Enabling the Next Frontier of Autonomous Intelligence Systems, presents a comprehensive and systematic exploration of Agentic AI as an emerging paradigm in artificial intelligence. Agentic AI refers to artificial systems designed with the capacity for autonomous decision-making, goal-directed behavior, contextual reasoning, and persistent interaction with their environments. Unlike traditional AI systems that respond to inputs in isolation, agentic systems maintain internal state, evaluate alternatives, and act intentionally within defined constraints.

International Standard Book Number:978-81-965700-2-6

Table of Content

PREFACE

ACKNOWLEDGEMENT

INTRODUCTION

EXECUTIVE SUMMARY

ABSTRACT

NOVELTY STATEMENT

PART I – Foundations of Agentic Intelligence

1. Evolution of Artificial Intelligence Paradigms
2. From Reactive Systems to Agentic Architectures
3. Autonomy, Agency, and Intentionality in AI
4. Cognitive Models Underpinning Intelligent Agents
5. Limitations of Traditional AI Systems

PART II – Theoretical Models of Agency

1. Formal Definitions of Agency in Computational Systems
2. Decision Theory and Rational Agents
3. Multi-Objective Optimization and Goal Alignment
4. Learning vs Reasoning in Agentic Systems
5. Bounded Rationality and Real-World Constraints

PART III – Agent Architectures

1. Symbolic Agents
2. Reactive and Deliberative Architectures
3. Hybrid Cognitive Architectures
4. Memory, Context, and Long-Term State
5. Hierarchical and Modular Agent Design

PART IV – Agentic Learning Mechanisms

1. Reinforcement Learning as Agency
2. Planning, Search, and Exploration
3. Self-Improving Agents
4. Meta-Learning and Adaptation
5. Continual Learning in Autonomous Systems

PART V – Multi-Agent Systems

1. Coordination and Cooperation
2. Competition and Game-Theoretic Agents
3. Emergent Intelligence
4. Swarm Intelligence and Collective Behavior
5. Distributed Decision-Making

PART VI – Agentic AI Systems Engineering

1. Agent Lifecycle Management
2. Scalability and Performance Constraints
3. Reliability and Fault Tolerance
4. Human-in-the-Loop Architectures
5. Observability and Control

PART VII – Agentic AI and Autonomy

1. Degrees of Autonomy
2. Trust and Explainability
3. Alignment and Safety Challenges
4. Self-Governance and Constraint Enforcement
5. Failure Modes in Autonomous Agents

PART VIII – Ethics, Policy, and Governance

1. Ethical Frameworks for Agentic Systems
2. Regulatory Perspectives on Autonomous AI
3. Accountability and Responsibility
4. Bias, Fairness, and Transparency
5. Risk Management and AI Governance