Agentic Artificial Intelligence
Agentic Artificial Intelligence
Building Autonomous, Goal-Driven Systems
About the Book
The future of Artificial Intelligence isn't just intelligent — it's agentic.
We are entering a new era where artificial intelligence doesn't just respond to prompts but initiates actions, makes decisions, and pursues goals. Agentic AI is your essential guide to designing and deploying these next-generation autonomous systems.
This book dives deep into:
- Core principles of agentic behavior and decision-making
- Architectures and frameworks for building intelligent agents
- Integration with tools, environments, and real-world systems
- Ethical and practical challenges of autonomous AI
Whether you're a developer, researcher, technologist, or simply AI-curious, Agentic AI will help you navigate the rapidly evolving landscape of self-directed machines — and shape how we collaborate with them.
About the Author
Table of Contents
- Introduction: The Rise of Agentic AI
- Overview of the Book’s Goals
- What is “Agentic AI”?
- The Difference Between Traditional AI and Agentic Systems
- Why Now? The Convergence of Models, Tools, and Autonomy
- Take Away
- Understanding Agency
- Definition of agency in philosophy, psychology, and AI
- Characteristics of Agentic Systems: Autonomy, Goal-Seeking, and Adaptability
- Human vs. Artificial Agency
- Take Away
- Historical Evolution
- From Expert Systems to Intelligent Agents
- The Influence of Symbolic AI, Machine Learning, and Reinforcement Learning
- Milestones in Autonomous AI Development
- Take Away
- Core Components of Agentic AI
- Goals and Intentions
- Planning and decision-making
- Memory and learning
- Action and environment interaction
- Evaluation and reflection
- Chapter 4: Modular Architectures
- Agent loops: Perceive → Plan → Act → Learn
- Architectures: BDI (Belief-Desire-Intention), reactive vs. deliberative
- Layered and hybrid designs
- Embedding Goals and Autonomy
- Representing and prioritizing goals
- Goal conflict and arbitration
- Balancing autonomy with alignment
- Memory and Contextual Awareness
- Episodic vs. semantic memory
- Context windows and vector databases
- Self-reflection and state-tracking
- Large Language Models as Agents
- Capabilities and limitations of LLMs
- Prompt engineering for agency
- Tool use and decision support
- Planning and Reasoning Tools
- Chain-of-thought prompting
- Classical planners vs. learned strategies
- Open-source agents: AutoGPT, BabyAGI, LangGraph
- Interaction with Tools and Environments
- Tool use APIs, plugin systems, and environments
- Embodiment: web agents, robotic agents, virtual avatars
- Simulated vs. real-world action loops
- Personal Agents
- Productivity assistants and schedulers
- Life coaches, wellness agents
- Customization and alignment to user values
- Enterprise and Industrial Agents
- Autonomous business processes
- Research, analysis, and decision support agents
- Multi-agent collaboration
- Multi-Agent Systems and Ecosystems
- Swarms and distributed intelligence
- Coordination and negotiation
- Emergent behaviors and risks
- Ethical Design of Agentic Systems
- Safety, control, and oversight
- Transparency and explainability
- User consent and trust
- Alignment and Goal Drift
- Instrumental convergence and unintended consequences
- Human-in-the-loop design
- Ongoing goal calibration
- The Future of Human-Agent Collaboration
- Augmentation vs. replacement
- Societal implications of widespread agentic AI
- Philosophical questions: Can agents be “conscious”? Do they deserve rights?
- From Tools to Partners
- Recap of what it means to build agentic AI
- What developers, leaders, and society need to prepare for
- Call to action: shaping the future of autonomy with care
- Introduction: The Rise of Agentic AI
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