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The No-Code AI Agent Builder Series

Correa J: Build AI Agents Without Coding

Build AI Agents Without Coding

A Beginner's Guide to Custom GPTs, Workflows & Automations

Palgrave Studies in Classical Liberalism

This book presents a classical liberal perspective on the governance and economic implications of artificial intelligence. As generative AI transforms industries and institutions, this book reframes urgent debates on safety, regulation, labour, and autonomy through the lens of classical liberal economic and political philosophy.
Drawing on Austrian economics, liberal growth theory, and institutional economics, this book argues that classical liberalism and its philosophical framework has not only historically inspired the design of artificial intelligence but also remains a critical guide for its future development and application. From neural networks as emergent orders to AI agents as capital goods, the book explores how liberal principles can guide innovation, mitigate risk, and preserve individual rights. Chapters examine interpretability, the future of work, open-source commons, and the calculation debate, engaging with theorists such as Hayek, Kirzner, Simon, and Schumpeter. The book proposes polycentric governance models for AI safety and rights protection, emphasizing pluralism, contestability, and due process over precautionary control.
This book will be an essential resource for scholars in political economy, law and economics, technology policy, and digital governance. It will also appeal to a broader readership interested in academically rigorous, liberal approaches to the challenges and opportunities of the intelligence explosion.

This book presents a classical liberal perspective on the governance and economic implications of artificial intelligence. As generative AI transforms industries and institutions, this book reframes urgent debates on safety, regulation, labour, and autonomy through the lens of classical liberal economic and political philosophy.Drawing on Austrian economics, liberal growth theory, and institutional economics, this book argues that classical liberalism and its philosophical framework has not only historically inspired the design of artificial intelligence but also remains a critical guide for its future development and application. From neural networks as emergent orders to AI agents as capital goods, the book explores how liberal principles can guide innovation, mitigate risk, and preserve individual rights. Chapters examine interpretability, the future of work, open-source commons, and the calculation debate, engaging with theorists such as Hayek, Kirzner, Simon, and Schumpeter. The book proposes polycentric governance models for AI safety and rights protection, emphasizing pluralism, contestability, and due process over precautionary control.This book will be an essential resource for scholars in political economy, law and economics, technology policy, and digital governance. It will also appeal to a broader readership interested in academically rigorous, liberal approaches to the challenges and opportunities of the intelligence explosion.

A Classical Liberal Approach

Artificial Intelligence

We are entering an economy where AI agents are the primary workforce - not assistants to humans, but autonomous actors that handle sales, code, support, security, and operations. This book names the infrastructure layer before the world sees it.
Atin Agarwal presents the dependency layer thesis - the infrastructure play of the next decade - alongside 15 falsifiable predictions about the agent economy through 2030, each with specific criteria and timeframes. Drawing from real numbers from live agent businesses, not theory but deployed systems generating revenue, he reveals how a solo operator runs 8 AI-agent-powered ventures from India.
This book is a practitioner's playbook for tech founders evaluating agent-first business models, CTOs building agent infrastructure, VCs looking for a framework to evaluate agent-economy startups, and solo operators exploring the one-person conglomerate model.

Why the Next Trillion-Dollar Industry Has No Employees

The AI Agent Economy

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

Artificial Intelligence is no longer a technology of the future-it is the competitive advantage of today.
In Dominate the Future with AI, Shiv Darshan Sharma provides a practical roadmap for entrepreneurs, professionals, freelancers, startups, and business leaders who want to harness AI to create wealth, accelerate growth, and build sustainable competitive advantages.
Through actionable frameworks, real-world case studies, AI business models, prompt engineering techniques, autonomous AI agents, monetization strategies, and step-by-step implementation plans, this book transforms complex AI concepts into practical business opportunities.
Inside this book, you will discover:
¿ How to identify profitable AI opportunities
¿ The essential AI skills required for future success
¿ Prompt engineering techniques that deliver results
¿ How to build and deploy AI-powered solutions
¿ AI monetization and wealth creation strategies
¿ Business transformation roadmaps using AI
¿ Career advancement strategies in the age of AI
¿ Real-world examples from startups, freelancers, and enterprises
Whether you are a business owner, working professional, consultant, student, or aspiring entrepreneur, this book will help you move beyond theory and start using AI as a powerful tool for growth, innovation, and financial success.
The future belongs to those who learn, adapt, and act. The time to dominate with AI is now.

Your Blueprint for Wealth, Work & Business Success

DOMINATE THE FUTURE WITH AI

Explore how autonomous AI agents are transforming financial markets and decision-making In Agentic Finance: Mathematics, Architecture, and Governance for AI Agents in Finance, Miquel Noguer Alonso and David Pacheco Aznar examine the emerging field where autonomous AI systems operate independently in financial contexts. The book explores how agentic technologies - AI systems capable of independent reasoning, planning, and action - are reshaping how financial institutions approach analysis, trading, and risk management. The book explores the mathematical foundations powering autonomous AI agents, from the algorithms enabling independent decision-making to the frameworks governing multi-agent financial systems. Readers gain insight into how innovators are applying these technologies across trading, portfolio management, and market analysis, along with the technical principles driving this transformation. Readers will also discover:&nbsp; <ul> <li>The core mathematical concepts underlying autonomous AI systems, including reinforcement learning, optimization, and probabilistic reasoning frameworks</li> <li>How leading innovators are deploying agentic AI across trading, risk assessment, portfolio optimization, and financial forecasting applications</li> <li>Technical architectures enabling AI agents to operate autonomously while maintaining appropriate oversight and governance in financial environments</li> <li>Real-world applications demonstrating how autonomous systems analyze markets, execute strategies, and adapt to changing conditions</li> <li>The evolving regulatory and ethical landscape surrounding autonomous AI decision-making in high-stakes financial contexts</li> </ul> Agentic Finance serves quantitative analysts, financial technologists, AI researchers, portfolio managers, and business leaders seeking to understand how autonomous AI systems are transforming financial services. It provides the technical foundation needed to evaluate and implement agentic technologies.

Mathematics, Architecture, and Governance for AI Agents in Finance

Agentic Finance

Artificial Intelligence is no longer a future concern—it’s a present-day disruptor. As vendors and partners increasingly adopt AI-enabled products and services, third-party and supply-chain risk professionals face a new challenge: managing a rapidly evolving risk landscape with limited guidance. This book delivers the clarity and structure needed to navigate that complexity.
Designed for business professionals—not just technologists—this practical guide walks readers through the full lifecycle of AI-related vendor risk, from intake to offboarding. With hands-on examples, actionable templates, and real-world use cases, it equips readers to assess and manage AI risk confidently, even in environments without dedicated IT security teams. It also explores how AI can be used within TPRM programs to enhance efficiency and accuracy.
As regulatory frameworks around AI continue to emerge and evolve, this book provides timely insight into compliance expectations and how they impact risk programs and leadership. Whether you're a seasoned risk manager or new to the field, you’ll find concise, jargon-free guidance that respects your time and delivers immediate value.
AI may be complex, but managing its risk doesn’t have to be. This book transforms confusion into clarity, helping you turn disruption into opportunity—and build a resilient, future-ready risk management program.
What You Will Learn:
How to measure risk and risk-based approaches.
Third-party risk frameworks.
How to assess the risk of AI with vendors.
Major AI risk management frameworks.
Regulatory guidance for AI--a country-by-country analysis.

Who This Book Is for:
· C-level suite: this is not designed to be overly technical but covers material enough to allow this level to be conversant in strategy and leadership needs to success.
· Director-level in Cyber and IT: this level of personnel are above the individual contributors (IC) and require the information in this book to translate the strategy goals set by C-suite and the tactics required for the ICs to implement and govern.
· GRC leaders and staff: the focus on governance in this book will assist these teams to better understand the strategy and technologies to determine the governance models needed. 
· Individual Contributors: although not designed to be a technical manual for engineering staff, it does provide a Rosetta Stone for them to understand how important strategy and governance are to

Artificial Intelligence is no longer a future concern—it’s a present-day disruptor. As vendors and partners increasingly adopt AI-enabled products and services, third-party and supply-chain risk professionals face a new challenge: managing a rapidly evolving risk landscape with limited guidance. This book delivers the clarity and structure needed to navigate that complexity.Designed for business professionals—not just technologists—this practical guide walks readers through the full lifecycle of AI-related vendor risk, from intake to offboarding. With hands-on examples, actionable templates, and real-world use cases, it equips readers to assess and manage AI risk confidently, even in environments without dedicated IT security teams. It also explores how AI can be used within TPRM programs to enhance efficiency and accuracy.As regulatory frameworks around AI continue to emerge and evolve, this book provides timely insight into compliance expectations and how they impact risk programs and leadership. Whether you're a seasoned risk manager or new to the field, you’ll find concise, jargon-free guidance that respects your time and delivers immediate value.AI may be complex, but managing its risk doesn’t have to be. This book transforms confusion into clarity, helping you turn disruption into opportunity—and build a resilient, future-ready risk management program.What You Will Learn:How to measure risk and risk-based approaches.Third-party risk frameworks.How to assess the risk of AI with vendors.Major AI risk management frameworks.Regulatory guidance for AI--a country-by-country analysis. 
 Who This Book Is for:
 
 ·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
 C-level suite
 : this is not designed to be overly technical but covers material enough to allow this level to be conversant in strategy and leadership needs to success.
 
 ·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
 Director-level in Cyber and IT
 : this level of personnel are above the individual contributors (IC) and require the information in this book to translate the strategy goals set by C-suite and the tactics required for the ICs to implement and govern.
 
 ·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
 GRC leaders and staff
 : the focus on governance in this book will assist these teams to better understand the strategy and technologies to determine the governance models needed.&nbsp; 
 
 ·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 
 Individual Contributors
 : although not designed to be a technical manual for engineering staff, it does provide a Rosetta Stone for them to understand how important strategy and governance are to

Solutions for Assessing and Managing Your AI Vendors and Systems

AI and Third-Party Risk

In a world where AI is rapidly becoming embedded in everyday tools, this book helps you cut
through the noise to understand Microsoft’s growing Copilot ecosystem and confidently choose the
right tool for the right task.


You’ll start by untangling the different Copilot experiences in Microsoft 365. From there, you’ll explore
agents, learning why they matter and how they enable powerful automation without requiring deep
coding skills.


Along the way, you’ll get plain-language explanations of the buzzwords that make this space feel
overwhelming and a realistic view of what AI can and can’t do. By the end of the book, you won’t just
know the difference between a bot, a workflow, and an agent; you’ll know exactly when each is the right
choice and how to apply them with confidence.


What You will Learn:


• Choose the right Copilot or agent for specific business scenarios
• Understand how large language models work behind the scenes
• Interpret common AI terminology without the jargon
• Distinguish between bots, workflows, and agents and when to use each
• Identify strong AI use cases and avoid common pitfalls

 

Who This Book Is For

Business users, developers and Power Platform makers

In a world where AI is rapidly becoming embedded in everyday tools, this book helps you cut
 
 through the noise to understand Microsoft’s growing Copilot ecosystem and confidently choose the
 
 right tool for the right task.
 
 You’ll start by untangling the different Copilot experiences in Microsoft 365. From there, you’ll explore
 
 agents, learning why they matter and how they enable powerful automation without requiring deep
 
 coding skills.
 
 Along the way, you’ll get plain-language explanations of the buzzwords that make this space feel
 
 overwhelming and a realistic view of what AI can and can’t do. By the end of the book, you won’t just
 
 know the difference between a bot, a workflow, and an agent; you’ll know exactly when each is the right
 
 choice and how to apply them with confidence.
 What You will Learn: 
 • Choose the right Copilot or agent for specific business scenarios
 
 • Understand how large language models work behind the scenes
 
 • Interpret common AI terminology without the jargon
 
 • Distinguish between bots, workflows, and agents and when to use each
 
 • Identify strong AI use cases and avoid common pitfalls
 &nbsp;Who This Book Is ForBusiness users, developers and Power Platform makers

Navigating Copilot and Agents in Microsoft 365

The Copilot Compass

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

Architecting and Governing Autonomous AI Agent Ecosystems

The Agentic Enterprise Strategy

A Guide for Humans, Engineers, and Enterprises

AI Powered Digital Twins

Animals

Der Praxisleitfaden für leistungsstarke KI-Agentensysteme

- Lernen Sie die zentralen Merkmale von KI-Agenten kennen und verstehen Sie, wie sie sich von klassischen Machine-Learning-Systemen unterscheiden

- Entdecken Sie die wichtigsten Komponenten und Designprinzipien von Agentensystemen

- Erfahren Sie, welche Tools und Frameworks sich für Entwicklung, Deployment und Absicherung von Agenten gegen Risiken eignen

- Entwerfen und implementieren Sie maßgeschneiderte KI-Lösungen, die die Effizienz und Innovation Ihrer Anwendungen steigern

Dieses Buch bietet einen klar strukturierten Rahmen, um robuste Agentensysteme zu entwickeln. Es zeigt, was moderne KI-Agenten ausmacht, welche Strategien für Planung, Ausführung und Tool-Nutzung funktionieren und wie Agenten durch Feintuning und Transfer Learning aus Erfahrung lernen. Das Buch liefert damit fundierte Antworten auf typische Fragestellungen aus der Praxis.

Michael Albada führt Sie in die Skalierung von Single- zu Multiagentensystemen ein und beleuchtet bewährte Muster wie demokratische, herarchische und Actor-Critic-Ansätze. Er vermittelt, wie man Agenten mit geeigneten Metriken bewertet, in der Produktion überwacht und sicher, ethisch und regelkonform betreibt. Prägnante Fallstudien aus Kundensupport, Recht, Werbung und Code-Review bieten das nötige Rüstzeug, um KI-Agentensysteme wirkungsvoll einzusetzen.

Der Praxisleitfaden für leistungsstarke KI-Agentensysteme<ul><li>Lernen Sie die zentralen Merkmale von KI-Agenten kennen und verstehen Sie, wie sie sich von klassischen Machine-Learning-Systemen unterscheiden</li><li>Entdecken Sie die wichtigsten Komponenten und Designprinzipien von Agentensystemen</li><li>Erfahren Sie, welche Tools und Frameworks sich für Entwicklung, Deployment und Absicherung von Agenten gegen Risiken eignen</li><li>Entwerfen und implementieren Sie maßgeschneiderte KI-Lösungen, die die Effizienz und Innovation Ihrer Anwendungen steigern</li></ul> Dieses Buch bietet einen klar strukturierten Rahmen, um robuste Agentensysteme zu entwickeln. Es zeigt, was moderne KI-Agenten ausmacht, welche Strategien für Planung, Ausführung und Tool-Nutzung funktionieren und wie Agenten durch Feintuning und Transfer Learning aus Erfahrung lernen. Das Buch liefert damit fundierte Antworten auf typische Fragestellungen aus der Praxis.Michael Albada führt Sie in die Skalierung von Single- zu Multiagentensystemen ein und beleuchtet bewährte Muster wie demokratische, herarchische und Actor-Critic-Ansätze. Er vermittelt, wie man Agenten mit geeigneten Metriken bewertet, in der Produktion überwacht und sicher, ethisch und regelkonform betreibt. Prägnante Fallstudien aus Kundensupport, Recht, Werbung und Code-Review bieten das nötige Rüstzeug, um KI-Agentensysteme wirkungsvoll einzusetzen.

Entwurf, Implementierung, Monitoring

KI-Agenten entwickeln

The Proven Path From AI Tools to Peak Productivity

Amplified

Build AI Agents Without Coding

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