Agentic AI For Dummies

Name: Agentic AI For Dummies
Brand: Wiley-Scrivener
Price: 22.99 EUR
Availability: OnlineOnly

Pam Baker(Autor*in)

Wiley-Scrivener (Verlag)

1. Auflage

Erschienen am 10. Dezember 2025

354 Seiten

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978-1-394-37962-0 (ISBN)

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Inhalt

Intro
Title Page
Copyright Page
Table of Contents
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Beyond the Book
Where to Go from Here
Part 1 Understanding Agentic AI
Chapter 1 Introducing Agentic AI
Defining Agentic AI
Moving toward AGI with Agentic AI
Noting that agentic systems already exist
Reasoning as AI's Way Forward
Exploring philosophy, reason, and fear
Putting AI reasoning into perspective
Recognizing the operational challenges of Agentic AI
Differentiating between AI Agents and Agentic AI
Meeting examples of agents and agentic systems
Seeing what agents and Agentic AI systems have in common
Recognizing the distinctions between agents and Agentic AI systems
Mapping the Path from Prompt Engineering to AI Autonomy
Engineering your prompts for successful AI interaction
Controlling AI system actions with effective prompting
Viewing prompting as foundational
The Budding Agentic AI Web
Expanding the duty of agents online
Scaling up to a citywide, nationwide, and global reach
Following the Shift to A-Commerce
Recognizing the fallibility of AI answers
Reducing specific website interaction
Relying on personal AI shoppers
Optimizing commerce sites for AI
Navigating the transition to A-commerce
Chapter 2 Peeking Inside the AI Agent Mind
Linking the Fundamental Building Blocks
Identifying Agentic AI building blocks
Enter the overseers
Exploring Reasoning, Memory, and Goal Setting
Assessing Agentic AI reasoning
Considering AI's limited intelligence
Recognizing its intentional design
Evaluating Agentic AI memory
Adding memory to a system's design
Blending memory and reasoning in the design
Getting (too) personal with memory
Grasping Agentic AI goal setting
Understanding Adaptive Behavior and Self-Directed Learning
Dissecting adaptive behavior
Delving into self-directed learning
Learning more than new information
Examining other aspects of meta-learning
Directing Agentic AI
Talking it over with Agentic AI
Continuing direction over the AI's work
Completing the mission and next steps
Interacting with GenAI and Agentic AI
Combining Generative Abilities and Real-Time Decision-Making
Expanding on content generation
Applying agentic capabilities to complex interconnections
Operating autonomously across time
Staying the course in a changing environment
Chapter 3 Meeting Agentic AI Core Technologies
Driving Multi-Agent Coordination and Planning
Computational complexity and task decomposition
Specialized expertise and division of labor
Scalability and fault tolerance
Distributed information and resources
When resources can't be moved
Apply the multi-agent approach
Emerging coordination mechanisms
Communication and shared understanding
Connecting Contextual Awareness and Situational Reasoning
World modeling
Perception and sensor fusion
Memory architectures
Theory of mind modeling
Communication protocols and intent signaling
Planning and goal-conditioned learning
Distributed coordination and federated learning
Self-Correcting Continuous Improvement
Improving by failing and adjusting
Correcting more than just mistakes
Shifting to Multimodal Input and Cross-Domain Functionality
Contrasting reactive and proactive operation
Exploiting multiple streams of input
Enabling the growth of multimodal systems
Streamlining Integrations Using New Protocols
Model Context Protocol (MCP)
Seeing how MCP works
Recognizing MCP's limitations
Agent Network Protocol
Agent2Agent protocol (A2A)
Fostering agent communication
Barriers to agent collaboration
Agent Communication Protocol
Incorporating internet methods
Focusing on precision
Building AI Agents
Choosing technical architecture approaches
Building from scratch
Using agentic frameworks
Using AI agent-building platforms
Supporting system development, regardless of method
Building without coding
Recognizing both benefits and drawbacks
Looking over types of platforms
Building with coding
From-scratch options (or not)
Framework options
Chapter 4 Interacting with Agentic AI
Mistaking AI as a Colleague Creates Errors
Establishing the AI-as-tool mindset
Discovering how to direct Agentic AI
Comparing Context Engineering to Prompt Engineering
Seeing why you need both practices
Understanding the fundamental differences in engineering methods
Examining the basics of context engineering
Providing comprehensive awareness
Interconnecting system components
Augmenting context engineering with prompt engineering
Incorporating prompt engineering in Agentic AI
Prompting and tool integration
Maintaining agent behavior
Evolving Voice, Intent, and Semantic Interface Design
Voicing your intent with AI
Interpreting meaning with semantic interfaces
Rising Hyper-Real AI Avatars
Personalizing Workflows
Taking informed actions
Adapting to (and for) user's roles
Tailoring output based on user feedback or co-agent needs
Shifting from Apps to Agents
Reducing the complexity of our world
Dying app stores
Reimagining the internet through agents
Grappling with AI-related challenges
Forbidding AI Agents from Running Certain Machines
Recognizing the system design differences
Addressing the timing issue
Noting the current lack of Agentic AI transparency
Exposing the interface issues
Examining data integrity
Part 2 Getting Started on the Agentic AI Path
Chapter 5 Planning for the Shift to Agentic AI Systems
Comparing Generative AI to Agentic AI with Goals in Mind
Considering AI strengths and oversight required
Seeing the power of combined AI
Thinking Through an Agentic AI Plan
Recognizing the five pillars of Agentic AI planning
Setting up SMART goals and detailed follow-up
Double-checking your plan
Following the Steps for Planning and Implementing Agentic AI
Step 1: Establishing strategic intent
Making a strategic-intent commitment
Examining the components of strategic intent
Step 2: Evaluating readiness
Step 3: Identifying high-impact use cases
Linking Agentic AI deployments to business priorities
Looking for value and success
Choosing a pilot project
Step 4: Designing the pilot framework
Step 5: Building or integrating Agentic AI systems
Implementing technical strategies
Integration architecture and data flow
Monitoring and observability systems
Deployment and operational considerations
Step 6: Running, measuring, and refining
Taking the right measurements
Refining operational system models
Step 7: Expand and scale
Facing challenges of Agentic AI expansion
Starting small and scaling up
Meeting organizational complexity
Step 8: Establishing governance and trust
Step 9: Upskilling your workforce
Step 10: Reimagining business models and value creation
Chapter 6 Sampling Sector Use Cases for AI Agents
Developing Healthcare, Diagnostics, and Pharmaceuticals
Personalizing with AI treatment agents
Dispatching AI medical imaging agents
Accelerating clinical trial patient matching
Upskilling surgeons for robotic surgery
Building Business Operations and Decision Support
Simulating economies with AI financial modeling agents
Optimizing procurement and negotiations with AI agents
Auditing with ever-present internal AI agents
Adding AI Agents for Marketing, Customer Experience, and Inventory
Deploying AI marketing agents
Engaging AI customer experience agents
Building AI inventory optimization agents
Creating Content: Writing, Design, and Media
Collaborating with AI writing agents
Designing with AI agents
Editing with post-production agents
Reinventing Education
Personalizing AI learning agents
Contextually aware tutoring agents
Upgrading administration automation
Chapter 7 Considering Risks, Ethics, and Hard Questions
Losing Human Skill and Baseline Knowledge
Seeing the scope of AI-involved skill loss
Delegating work to AI agents responsibly
Designing agentic systems to mitigate loss of competency
Autonomy versus Control: Establishing Who's in Charge
Transportation: Lessons in control
Healthcare: Human oversight in life-and-death decisions
Finance: Algorithms, autonomy, and accountability
Education: Autonomy and human agency
Discovering Alignment Problems and Value Misfires
Value learning drift
Everyday misfires
High-stakes alignment challenges
Why achieving alignment is so hard
Detecting misfires
Value alignment as a collective effort
Missing Transparency and Explainability
Looking for transparency in Agentic AI reasoning
Addressing explainability
Revisiting Bias, Justice, and Inclusivity
From hidden bias to active misfires
Justice as more than accuracy
Inclusivity as a design imperative
Cultural and contextual sensitivity in autonomous operations
Hallucinating AI Agents at the Wheel?
Addressing AI hallucinations
Aiding AI with clear direction and human oversight
Part 3 Agentic AI in the Real World
Chapter 8 Reshaping Work with Agentic AI
Shaping Human Minds and Mindsets
Heeding a warning from MIT
Taking an active approach to using AI
Augmenting Human Judgment and Creativity
Redefining Job Roles and Workflows
Seeing the big picture of adapting jobs
Changing the human role in customer service
Freeing financial analysts from data collection
Collaborating for efficiency in creative or marketing fields
Fine-tuning healthcare teams' interactions
Collaborating with Shared Intelligence
Pooling abilities to achieve combined intelligence
Accessing the same operational context
Maintaining human oversight
Trusting in collaborative intelligence
Drawing on past memory
Feeding it all back
Surviving the Transition to the Agentic AI Workplace
Using tools for sophisticated job hunting
Finding AI tools for financial relief
Reducing expenses by using AI agents
Making money (maybe) by using AI agents
Chapter 9 Predicting Agentic AI's Economic Impact
Predicting Productivity Gains and Automation Impacts
Current progress and productivity gains
Expected productivity gains
Deciding Who or What Gets the Job
Understanding Agentic AI's core capabilities
Discovering human advantage in complex contexts
Identifying where Agentic AI excels
Strategies for Delegating Tasks to Humans or AI Agents
Teaming, rather than competing
Making a framework for task assignments
Avoiding common pitfalls
Determining Impact on the Future of Work
Rising new industries, roles and disciplines, and business models
The industries
The roles
The business models
Reinventing digital marketplaces and economies
For the buyers
For the sellers
And beyond
Chapter 10 Building Agentic Systems Responsibly
Developing Design Principles for Safe Autonomy
Looking at known risks for design decisions
Containing AI behavior
Recognizing outside threats to AI
Building a design doctrine
Navigating regulatory frameworks and global standards
Tying Together Design Principles
Placing limits on agentic systems' reach
Instilling human-in-the-loop safeguards
Promoting trustworthy outcomes
Incorporating ready-made tools
Testing, evaluation, and red teaming
Training AI Agents
Pre-training models for Agentic AI systems
Adding the proactive piece
Refining in simulated or real environments
Building in protections and ethical practices
An Agentic AI training example
Evolving beyond Data Training: World Models
Operating in 3D
Learning within the real world
World models already in operation
The pros and cons of world models
Part 4 Exploring Myths and Realities
Chapter 11 Dispelling Common Agentic AI Misconceptions
Agentic AI = Fully Autonomous, Uncontrollable Systems
Acknowledging the boundaries of Agentic AI
Avoiding unrealistic mandates
Recognizing the frameworks that surround Agentic AI
Emergent, unpredictable behaviors aren't chaotic autonomy
It's Just a Fancier Chatbot
Witnessing the development of capabilities
Acknowledging the importance of a conversational interface
Breaking down the real differences
Agents Replace People
Taking the businesses' perspective
Figuring where the costs lie
Following the media's spin
Seeing how AI can take over work
Only Giant Companies Can Use Agentic AI
Noting the costs involved
Watching a trend toward less-costly AI
It's the Same as Traditional Automation
When traditional automation works
When to opt for Agentic AI
Combining automation and Agentic AI approaches
Agentic AI Requires Universe-Sized Datasets
Chapter 12 Upskilling for the Agentic Age
Knowing What to Learn and Unlearn
Becoming an AI agent manager
Ramping up your data literacy
Applying systems thinking
Weaving in ethics and governance
Enhancing creative problem-solving skills
Reframing old skills and habits
Roles for Technologists, Creative Professionals, and Leaders
Technologists: From builders to orchestrators
Creative professionals: Dictated creations and director roles
Leaders: From managers to ecosystem orchestrators
Lifelong Learning and Agile Adaptation
Developing a strategic learning framework
Building actionable learning strategies
Future-proofing your career
Ethical Fluency and Human Judgment
Understanding ethical fluency
Appreciating the value of human judgment
Creating your personal development plan
Emotional Intelligence versus Intuitive Intelligence
Chapter 13 Scoping the Future of Agency
Consciousness, Intent, and Artificial Goals
Recognizing the appearance of consciousness
Identifying the nature of artificial intent
Directing the power of Agentic AI goals
Raging at the machine is humans fighting with themselves
Asking Philosophical and Existential Questions about Agentic AI
Free will and determinism in AI agents
Exploring the free-will piece
Examining the idea of determinism
Making AI agents more predictable
Ensuring clear operations
Focusing on oversight
Proving and securing
Synthetic Agency and Collective Intelligence
Collective intelligence in multi-agent ecosystems
Swarm intelligence and decentralized agency
Possible Futures: Utopia, Dystopia, or Both?
Looking toward utopia
Hearing the dystopian alarms
Taking the middle road
Part 5 The Part of Tens
Chapter 14 Ten Surprising Ways Agentic AI Can Change Daily Life
You Stop Googling So Much
Errands Run Themselves
Your Calendar Coordinates For You
Your E-Mail Inbox Shrinks
You Make Better Decisions
You Have a Personal Finance Manager
You Get Fully Personalized Learning
You Don't Have to Shop
You Get a Travel Planner, Tour Guide, and Concierge
Your Digital Presence Manages Itself
Chapter 15 Ten Things Agentic AI Is Terrible at Doing
Understanding Human Emotion in Context
Making Moral or Ethical Judgments
Handling Novel, Unstructured Problems
Using Creative Intuition and Artistic Vision
Understanding the Bigger Picture
Reacting to Sudden, High-Stakes Emergencies
Balancing Competing Human Preferences
Building Human Trust and Rapport
Respecting Privacy and Boundaries
Saying "I Don't Know"
Chapter 16 Ten Bold Predictions about the Future of Agentic AI
Personal AI Agents Will Become as Common as Smartphones
Agentic AI Ecosystems Will Replace Traditional Apps
A-Commerce Will Surpass E-Commerce
SEO Will Evolve into AI Agent Optimization
Agentic AI Will Reshape Knowledge Work
New Legal and Ethical Frameworks Will Emerge for AI Agents
Agentic Education Will Become Mainstream
Digital Labor Markets for Agents Will Form
Healthcare Will See a Surge in AI-Driven Preventive Care
Humans Using Special AI Agents Will Lead Major Social Movements
Appendix
Agentic AI Readiness Checklist
Phases for Creating and Using Agentic AI Systems
Phase 1: Strategic foundation and use case selection
Phase 2: Technical architecture and integration planning
Phase 3: Governance and monitoring framework
Phase 4: Risk management and compliance
Index
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