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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

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

Build a resilient, high-performing organization by integrating agentic AI into strategy, operations, and decision-making. Building an Agentic Organization is a strategic guide for senior technology, AI and business leaders responsible for delivering value from agentic AI while managing its risks. As organizations move toward human and AI collaboration at scale, leaders must balance innovation with governance, adoption, and long-term economic impact. Written by experienced AI executive Geoffrey Schaefer, this book provides a comprehensive framework for designing an agentic organization by examining strategy, governance, and economics. It explores how to define the purpose of agentic AI, align it with business objectives, and create the structures needed to support safe, scalable adoption. Addressing both opportunity and risk, it equips leaders to navigate workforce concerns, reputational exposure, and the evolving role of AI agents in enterprise environments. You'll learn how to: - Define a clear strategy for integrating agentic AI - Establish governance frameworks to manage risk and accountability - Drive workforce adoption and address resistance to AI collaboration - Assess economic value and impact of agentic AI systems &gt;Featuring real-world examples from Leidos, JPMorgan Chase, SY Partners, and OMA, this book provides a practical roadmap for embedding agentic AI into the fabric of your organization while protecting value, trust, and long-term performance. Themes include: agentic AI, AI strategy, AI governance, AI transformation, enterprise AI, risk management

The Strategy, Governance, and Economics of Agentic AI

Building an Agentic Organization

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.
Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.
You Will:
<ul>
<li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li>
<li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li>
<li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li>
</ul>
&nbsp;
This book is for : Software Architects &amp; System Designers
&nbsp;

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.You Will:<ul><li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li><li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li><li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li></ul>&nbsp;This book is for : Software Architects &amp; System Designers&nbsp;

Architecting Scalable, Ethical, and Trustworthy Systems for Enterprise

Agentic AI Systems in Practice

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

Routledge Research in Corporate Law

Employing an interdisciplinary lens, this book examines the practices, legal frameworks, and regulatory challenges of corporate responsibility and accountability in emerging sectors, drawing on national, regional, and international case studies.In the current climate, business activities are debated and scrutinised worldwide due to their social, environmental, and human rights impacts. Concomitantly, corporations have been active agents of progress in the investment, creation, and dissemination of new technologies; fields in which the regulation of responsible business conduct is still developing. Aligning with current challenges of the United Nations Sustainable Development Goals, the book is divided into five parts and looks at AI and data, investment, production and consumption, energy, and climate change. It offers a compelling analysis of the convergences and divergences in business practices across diverse legal and regulatory frameworks. By examining the gap between law, regulation, and rapid advancements in emerging sectors and technologies, it provides actionable insights for corporate strategy, legal reform, and policy development, ensuring timeliness and practical relevance.The book will be of interest to researchers in the field of business and corporate law, corporate responsibility, and accountability.

Regulation and Practice in Emerging Sectors

Corporate Responsibility and Accountability in Business Law

Turn AI investment into scalable business impact by enabling your workforce to build the solutions they actually need. From AI Users to Solution Builders is a strategic guide for senior AI, technology, and transformation leaders who want to move beyond underperforming, centralized AI initiatives, and unlock real enterprise value. As organizations struggle to translate AI into measurable results, this book shows how shifting from top-down deployment to workforce-led solution building creates faster adoption, stronger ROI, and sustained innovation. Written by experienced AI leader Wei Manfredi, this book combines strategic insight with practical execution. It draws clear parallels between failed digital transformation efforts and current AI adoption challenges, while demonstrating how natural language interfaces are reshaping access to AI. By enabling non-technical professionals to design their own tools, organizations can embed AI directly into workflows, decision-making, and daily operations. You'll learn how to: - Shift from centralized AI models to workforce-driven innovation - Enable employees to build AI solutions for real workflows - Design scalable infrastructure for AI solution development - Apply change management to overcome resistance and drive adoption &gt;Featuring real-world examples from organizations including Lululemon, Google, Oracle, and JPMorgan Chase, this book provides a proven framework for embedding AI into everyday collaboration, accelerating innovation, and future-proofing your organization's competitive advantage. Themes include: AI strategy, AI adoption, workforce transformation, AI governance, enterprise AI, change management

Scaling AI Across Your Organization

From AI Users to Solution Builders

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

Security, Audit and Leadership Series

In an era where cyber threats evolve faster than traditional defenses can adapt, the need for a paradigm shift in cybersecurity auditing has never been more urgent. Transforming Cybersecurity Audit Practices with Agility and Artificial Intelligence (AI) presents a groundbreaking approach to modernizing audit methodologies.

Transforming Cybersecurity Audit Practices with Agility and Artificial Intelligence (AI)

An executive operating system for leading AI-driven strategic transformation Intelligence as a Commodity: Human Judgment as a Competitive Advantage delivers a structured leadership playbook for executives navigating AI adoption. Written by Rajeev Kapur, who built a billion-dollar business at Dell, and Jason Hunter, a senior consultant for the Blue Ocean Strategy Network, the book moves beyond AI trends to provide actionable frameworks for strategic foresight, organizational redesign, and ethical AI governance at the enterprise level. The book introduces flagship tools including HorizonAI for scenario-based strategic planning, HealthCheckAI for operational resilience assessment, and AI Deep Blue Strategy for AI-driven market innovation. Additional frameworks cover Executive Archetypes, the AI Leadership OS, AI Organizational OS, and Collective Intelligence Framework, each designed to shift organizations from reactive AI adoption to proactive, sustainable competitive positioning. Readers will also find: <ul> <li>Frameworks for assessing AI readiness and prioritizing initiatives across enterprise operations and strategy functions</li> <li>Tools for integrating human-AI collaboration while managing ethical, operational, and governance risk at scale</li> <li>Scenario-based strategic planning methods using HorizonAI to anticipate market shifts and technology disruptions</li> <li>Organizational redesign approaches for building agility and hybrid intelligence into existing leadership structures</li> <li>Guidance on embedding ethical AI governance and risk management into enterprise-wide adoption programs</li> </ul> Written for C-suite executives, directors, entrepreneurs, MBA participants, and consultants overseeing strategy, operations, and innovation, this book provides the frameworks and tools needed to lead AI-driven transformation with clear organizational design, ethical governance, and measurable business outcomes at the enterprise level.

Human Judgment as a Competitive Advantage

Intelligence as a Commodity

Shared Intelligence

Coming of Age

Apress Pocket Guides

This pocket guide is a concise, field-ready reference for IT leaders, administrators, and architects responsible for governing Microsoft 365 Copilot and Copilot Studio deployments. As AI evolves from simple assistants to autonomous digital colleagues, governance is no longer optional—it’s the backbone of secure, compliant, and value-driven adoption. Covering both platforms, this guide shows how to define effective policies, controls, and monitoring frameworks that protect organizational data while enabling innovation. Drawing from Microsoft’s latest governance models, enterprise case studies, and real-world best practices, it translates complex principles into clear, actionable steps to help organizations scale AI responsibly.
What You Will Learn:
<ul>
<li>Design and implement a governance framework for M365 Copilot and Copilot Studio

</li><li>Configure sensitivity labels, DLP policies, and lifecycle management to protect organizational data in AI workflows

</li><li>Apply environment strategy, role-based access control, and ALM to Copilot Studio agents

</li><li>Measure, monitor, and optimize AI agent performance, cost, and compliance

</li><li>Balance innovation with guardrails using models like Microsoft’s Zoned Governance
</li></ul>

This pocket guide is a concise, field-ready reference for IT leaders, administrators, and architects responsible for governing Microsoft 365 Copilot and Copilot Studio deployments. As AI evolves from simple assistants to autonomous digital colleagues, governance is no longer optional—it’s the backbone of secure, compliant, and value-driven adoption. Covering both platforms, this guide shows how to define effective policies, controls, and monitoring frameworks that protect organizational data while enabling innovation. Drawing from Microsoft’s latest governance models, enterprise case studies, and real-world best practices, it translates complex principles into clear, actionable steps to help organizations scale AI responsibly.What You Will Learn:<ul><li>Design and implement a governance framework for M365 Copilot and Copilot Studio</li><li>Configure sensitivity labels, DLP policies, and lifecycle management to protect organizational data in AI workflows</li><li>Apply environment strategy, role-based access control, and ALM to Copilot Studio agents</li><li>Measure, monitor, and optimize AI agent performance, cost, and compliance</li><li>Balance innovation with guardrails using models like Microsoft’s Zoned Governance</li></ul>

Practical Guardrails and Checklists for Secure, Responsible AI at Scale

Governance in Microsoft 365 Copilot & Copilot Studio

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

Bears & Mosquitoes

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