- Preface
- Who This Book Is For
- What This Book Covers
- How to Get in Touch
- Chapter 1:An Introduction to Agentic AI Systems
- 1.1:Understanding the fundamentals of Agentic AI
- 1.2:Exploring different types of Agent
- 1.3:Discovering Key Characteristics for AI Agent
- 1.4:Discovering Key Challenges of AI Agent
- 1.5:Summary
- 1.6:Further Reading
- Chapter 2:Evolution of Agentic AI Agents
- 2.1:Exploring the historical context and early AI applications
- 2.2:Exploring the progression of AI Agents from Rule-Based
- 2.3:Exploring the Evolution of Agentic AI Workflows
- 2.4:Exploring the evolution of Agentic intelligence for AI Agent
- 2.5:Understanding the comparative analysis of the Intelligent Agents deign framework
- 2.6:Discovering the Critical challenges and design considerations for Agentic AI systems
- 2.7:Summary
- 2.8:Further Reading
- Chapter 3:Exploring Best Practices for an Agentic AI Journey
- 3.1:Understanding the key characteristics of Agentic AI Systems
- 3.2:Discovering the Challenegs in Agentic AI and their impact on AI Agent
- 3.3:Exploring the Best Practices for Agentic AI Implementation
- 3.4:Exploring the Agentic AI Journey- A Design Thinking Approach
- 3.5:Summary
- 3.6:Further Reading
- Chapter 4:Agentic AI Architecture Pattern
- 4.1:Discovering the System Context of Agentic AI
- 4.2:Understanding the requirements of an Agentic AI System
- 4.3:Exploring the Key Design Principle for Agentic AI Systems
- 4.4:Exploring the Key Architecture Decisions for Agentic AI Systems
- 4.5:Designing the High-Level Design for an Agentic AI System
- 4.6:Summary
- 4.7:Further Reading
- Chapter 5:Agent Workflow and Multi-Agent Communication
- 5.1:Exploring Agentic Workflow Fundamentals
- 5.2:Designing Agent Workflow
- 5.3:Exploring Different Workflow Execution Patterns
- 5.4:Discovering the key elements for the Agentic Workflow
- 5.5:Understanding Multi-Agent Communication
- 5.6:Summary
- 5.7:Further Reading
- Chapter 6:Agent Operations (AgentOps) and Governance
- 6.1:Discovering the Evolution - DevOps to AgentOps
- 6.2:Exploring the key Component of AgentOps
- 6.3:Understanding the Lifecycle of AgentOps
- 6.4:Extending the Twleve-Factor method for AgentOps
- 6.5:Understanding operational pillars of AgentOps
- 6.6:Summary
- 6.7:Further Reading
- Chapter 7:Ethical Considerations and Compliance
- 7.1:Understanding Trustworthy AI
- 7.2:Understanding Fairness for AI Application
- 7.3:Understanding Explainability for AI Application
- 7.4:Understanding Robustness for AI Applications
- 7.5:Understanding Accountability for AI Application
- 7.6:Understanding Transparency for AI Application
- 7.7:Understanding Inclusive Design for AI Application
- 7.8:Summary
- Chapter 8:ACM AgentNet — Designing a Distributed Agentic AI Ecosystem Across the Enterprise
- 8.1:Discovering the ACME AgentNet Uue case
- 8.2:Establishing the Maturity roadmap – from zero to autonomous collaboration
- 8.3:Key challenges and common myths
- 8.4:Agentic AI and AgentOps Journey Checklist
- 8.5:Summary
- 8.6:Further Reading
- Preface
Emergence of Agentic AI
Architecting the Next Generation of Intelligent Systems
In a world where AI is evolving from passive models to proactive, decision-making entities, Agentic AI redefines how technology works with us—and for us. This book, Emergence of Agentic AI: Architecting the Next Generation of Intelligent Systems, is your guide to navigating and shaping this shift. Whether you're a student aiming to break into AI, a software architect building intelligent workflows, or a C-suite leader driving innovation, this book bridges the gap between concept and execution. It distills cutting-edge ideas into actionable frameworks, demystifies the architecture behind autonomous agents, and offers real-world insights into building scalable, trustworthy systems. From foundational design to operational excellence and ethical AI, this is not just a book—it’s a blueprint for participating in and leading the agentic AI revolution.
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About the Book
Emergence of Agentic AI: Architecting the Next Generation of Intelligent Systems is a comprehensive guide to designing, building, and operating intelligent agents that think, act, and adapt within complex systems. It begins by grounding readers in the fundamental principles of Agentic AI—what makes an AI agent truly autonomous and effective—then progresses through historical evolution, key architectural decisions, system-level design, workflow orchestration, and operational practices under the emerging field of AgentOps. Each chapter blends conceptual clarity with practical guidance, equipping you to tackle real-world challenges like drift, trust, multi-agent communication, and ethical compliance. Whether you're developing single-task agents or orchestrating distributed agent ecosystems, this book empowers you with the mindset, frameworks, and tools to engineer next-generation AI systems that are not only capable—but reliable, explainable, and human-centered.
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About the Author
Mansura Habiba
Mansura Habiba is the Principal Platform Architect for the AI and HPC Platform at IBM in Ireland, where she leads the development and continuous operation of AI analysis platforms. With over 15 years of experience in the software, infrastructure, and services industries, she specializes in AI-driven solutions and high-performance computing. Mansura holds a Ph.D. in Deep Learning from the University of Maynooth, Ireland, where she focused on deep learning and neural networks for time series analysis.
Mansura's work includes optimizing inventory management, automating processes, and improving data quality, leading to significant operational efficiency gains. Her contributions to the field are recognized through numerous awards, patents, and international journal and conference publications.
Mansura is also an accomplished author, previously working on cloud architecture and operations. Her latest book provides a comprehensive guide to AI platforms, covering everything from setup and data management to deploying advanced AI models and exploring future trends in AI operations. She actively shares her knowledge at industry conferences, on social media, and through various publications and speaking engagements.
Mansura also serves as a reviewer and board member for IEEE journals. She has filed several patents on Neural Networks, Workload Migration, Security, Blockchain, Quantum, Edge computing, and Sustainability, with more pending in Generative AI.
Going Forward, she will focus on guiding AI adoption across products to solve real-world problems. She prioritizes scalable, agile, and ethical AI solutions, leveraging her expertise in AI architecture and cloud operations to drive impactful innovation.
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