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Martin D: Bears & Mosquitoes

Bears & Mosquitoes

Building Trusted AI from the Inside Out - A Field Guide to AI That Earns Its Keep

Shared Intelligence

Coming of Age

Grasp how to deliver fully deployed and scalable, trusted and responsible AI and avoid expensive failed solutions and limited proof-of-concepts with this practical guide covering the lifecyle process of AI product development.

Grasp how to deliver fully deployed and scalable, trusted and responsible AI and avoid expensive failed solutions and limited proof-of-concepts with this practical guide covering the lifecyle process of AI product development.

From AI Ambition to Operational Success

Decision-Quality AI

AI and Constitutional Law in Europe

Democracy in the Age of Disinformation

How Leaders Turn GenAI into Exponential Results

Agentic Advantage

The healthcare industry is undergoing a transformative shift as artificial intelligence moves from isolated predictive models to autonomous, goal-driven agents capable of reasoning, planning, and adapting in real time.Agentic AI for Healthcare: Building Scalable Models for Production is the first comprehensive technical guide that focuses specifically on designing and deploying agentic AI systems within complex clinical and operational healthcare environments. Unlike traditional AI systems that passively infer outcomes, agentic AI involves systems that actively make decisions, coordinate with other agents, and take initiative toward long-term goals, making them ideal for dynamic and high-stakes healthcare settings. What sets this book apart is its deep technical focus tailored for computer scientists, architects, and AI engineers who are building real-world healthcare solutions. Each chapter bridges theoretical foundations with production-ready implementations using cloud-native technologies, distributed architectures, knowledge representation models, and secure deployment pipelines. With detailed case studies in oncology, vaccine management, and hospital operations, the book demonstrates how agentic AI can improve care delivery, reduce cognitive burden on clinicians, and automate routine workflows - all while meeting regulatory standards.As healthcare increasingly adopts AI-first strategies, this book fills a critical need by offering a structured, engineering-centered approach to building scalable, intelligent, and trustworthy healthcare systems.

Building Scalable Models for Production

Agentic AI for Healthcare

Studies in International Trade and Investment Law

This open access book examines the evolving role of international economic law in addressing two interrelated challenges of the 21st century: enabling digital development and bridging the digital divide. The rapid digitalisation of the economy has generated significant challenges, including a deepening global digital and data divide and a persistent lack of inclusive digital development. Confronting these challenges head on, this pioneering book brings together experts across different legal subfields, geographies, and disciplines to examine the role and relevance of international economic law in bridging the global digital divide and enabling digital development, particularly from the perspective of the Global South. The contributions are anchored in a distinctive analytical framework that traces future directions through which international economic law interacts with digital development, demonstrating how it may function as a constraint, fade into irrelevance, or eventually act as an enabler. The chapters focus on a broad range of areas, including international trade, investment, tax, development, competition, finance and related regulatory and policy domains; they consider multilateral, regional, and plurilateral frameworks; and draw on a plurality of perspectives from Africa, Asia, the Middle East, and Latin America. The authors employ a wide range of methodological approaches to address issues such as digital sovereignty and digital industrial policy, cross-border data flows and digital global value chains, competition in data markets, AI governance, financial inclusion, and the sustainability-digitalisation nexus. This book speaks to scholars, policymakers, and practitioners in international economic law, development studies, and technology law who seek theoretical and pragmatic pathways for reorienting international economic law as a tool for equitable, sustainable, and meaningful digital development. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.

Towards Digital Development

Reorienting International Economic Law for the Digital Economy

AI Oversight: A New Mandate for Corporate Directors and Executives

Artificial intelligence is transforming how companies make decisions, create value, and manage risk. Yet most governance frameworks were designed for a slower, more predictable era. As algorithms increasingly shape strategy, operations, and customer experience, corporate boards and executive leaders face a new responsibility: ensuring that intelligent systems are deployed with accountability, transparency, and trust.

In AI Oversight, global Chief Data Officer and technology leader Dr. Elena Alikhachkina introduces a practical framework for governing AI in modern enterprises. Drawing on decades of experience leading digital and data transformation across Fortune 500 organizations, and insights from more than 150 board directors and executives, the book reframes AI governance as a strategic leadership discipline rather than a technical exercise.

Through real-world examples, boardroom scenarios, and actionable oversight checklists, readers will learn how to move beyond AI hype and build the literacy, structures, and culture required to guide intelligent systems responsibly. The book explores how AI reshapes business models, introduces new fiduciary responsibilities, and demands a shift from compliance-based governance to reflective, learning-oriented oversight.

Written for corporate directors, executives, and senior leaders navigating digital transformation, AI Oversight provides the mindset and tools needed to steward innovation while protecting trust. In an age when machines learn faster than organizations can react, the true competitive advantage belongs to companies that can govern intelligence wisely.

A New Mandate for Corporate Directors and Executives

AI Oversight

Organizations have been promised that AI will revolutionize them. But what ends up happening is that AI ends up being introduced like standard software. When productivity is the only focus of enterprise AI, the real insights that AI can offer prove elusive. This book shows a better way. Written by leading AI expert and keynote speaker Richard Foster-Fletcher, The AI Gap explains why enterprise AI in most organizations isn't producing the insights that were promised. AI enters as a promise of new intelligence but passes through the procurement, security, legal and compliance systems built for standard software. These systems strip AI of its potential. This book traces how this happens, why it persists and what organization must change to capture the insights AI can deliver. This book will explain why the way AI is implemented keeps restricting its potential and what organization must change to capture the true value of AI. AI works best when it and employees are given the freedom to explore, challenge ideas and use AI in new and create ways. This book is about developing the structures, the environment, and the culture that allows AI to flourish in an organization as a key driver of insights rather than a simple productivity improvement.

How to Fix Enterprise AI and Get Real Insights

The AI Gap

Responsible AI Product Development shows how data, product and tech leaders can drive the development and deployment of trusted, responsible AI and navigate the associated risks and pitfalls. 

The book is designed for data, product and tech leaders who are seeking to implement structure and processes to ensure the development of responsible AI products compliant with best practice and key regulatory requirements. It guides readers from inception and team setup of AI through to use case development, full deployment, and monitoring, equipping them with the knowledge and tools they need to ensure an ethical and trusted product. It also helps readers understand the risks and challenges in AI product deployment, giving them guidance on what is important and how to avoid common mistakes.

Responsible AI Product Development offers coverage of the real lifecycle process, from thought exercise to ideation, development, creation, testing and delivery including post-delivery care and end of life management. It combines coverage of ethics and transparency with the technical, procedural and development-focused fundamentals, such as MLOPs, ModelOps, AIOps, TDD and SDLC guidance. It also considers key issues such as cybersecurity and DevSecOps, data architecture, collection and visualisation, team structure and processes, project documentation, business continuity and disaster recovery, AI governance and compliance and AI sustainability.

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.
The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, Agentic AI for Engineers equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. 
What You Will Learn

<li>A practical introduction to Machine Learning and Generative AI, tailored for engineers

</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.

</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops

</li><li>Understand different Agentic AI frameworks and their applications.

</li><li>Integrate agentic systems into real-world applications using hands-on coding examples

</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments




Who This Book Is For
Primary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. 
AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.
 


</li>

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
 By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, 
 Agentic AI for Engineers
 equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. &nbsp;
 What You Will Learn<ul><li>A practical introduction to Machine Learning and Generative AI, tailored for engineers</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops</li><li>Understand different Agentic AI frameworks and their applications.</li><li>Integrate agentic systems into real-world applications using hands-on coding examples</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments</li></ul>Who This Book Is ForPrimary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.&nbsp;&nbsp;

Architecting Goal-Driven Systems

Agentic AI for Engineers

How the rise of AI agents--and other forms of autonomous intelligence--will transform every aspect of how companies create value and compete.Companies have long operated with a basic assumption: intelligence is scarce. Talent is limited, expertise is expensive, and good judgment is hard to scale. But that world is ending.AI agents are bringing a new form of labor into the enterprise--one that can work autonomously, learn continuously, and scale instantly. Their embodied counterparts, from drones to humanoid robots, are doing the same in the physical world. Together, these systems are introducing a new economic reality: cognition is becoming cheap, elastic, and abundant.In Abundant Intelligence, Deloitte's Nitin Mittal and futurist Mike Walsh show what happens when intelligence becomes cheap and widely available. Drawing on inside access to the people leading AI transformations at the world's most powerful companies, they uncover how this shift is challenging every assumption about strategy, productivity, and leadership, and what organizations must do now to stay ahead.As abundance becomes the new normal, the leader's job shifts from collecting intelligence to directing it. Those who learn to harness this expanding workforce of digital labor will unlock new levels of speed, creativity, and advantage. For everyone else, the gap will widen fast.

How Digital Labor Will Rewrite the Rules of Business

Abundant Intelligence

Why AI is Exposing What Organizations Never Built

The Capability Debt

Bears & Mosquitoes

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