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AI Agents and Large Language Models for Economic Research with Python: Automating Causal Discovery, Model Building, Policy Simulation, and Real-Time Decision Support
English | May 31, 2026 | ASIN: B0H3J7ZS5H | 439 pages | Epub | 537.03 KB
Reactive PublishingAI Agents and Large Language Models for Economic Research with Python offers a practical, hands-on guide to integrating modern AI technologies into economic analysis and research workflows.This book explores how Large Language Models and autonomous AI agents can be leveraged alongside Python to enhance traditional economic research methods. It provides clear implementations for automating key tasks including causal discovery, econometric model building, policy simulation, and real-time decision support systems.What You'll Find Inside:Fundamentals of using LLMs for economic literature review, data interpretation, and hypothesis generationBuilding and deploying AI agents capable of executing multi-step economic research tasksPractical Python implementations for causal inference automationTechniques for constructing, validating, and simulating economic models at scaleMethods for creating real-time policy analysis and decision support toolsCode examples using current open-source libraries and frameworksBest practices for ensuring transparency, reproducibility, and robustness in AI-assisted economic researchWritten for economists, researchers, data scientists, and graduate students, this book bridges the gap between cutting-edge AI technologies and applied economic analysis. Rather than treating AI as a black box, it emphasizes understanding the underlying methods, limitations, and responsible implementation practices.Whether you are looking to accelerate your research pipeline, explore new analytical approaches, or incorporate AI agents into policy evaluation workflows, this guide provides the technical foundation and working code examples needed to begin.Clear. Technical. Reproducible.Ready to enhance your economic research capabilities with AI and Python.

AI Agents Revolution Build Autonomous Agents That Work 24x7
English | May 13, 2026 | ISBN: 9798235290853 | 201 Pages | EPUB (True) | 371.33 KB
The world is rapidly moving toward an era where machines are no longer limited to performing predefined tasks. Instead, they are becoming intelligent systems capable of thinking, planning, acting, and improving independently. This book, AI Agents Revolution Build Autonomous Agents That Work 24 7, is designed to help readers understand and master this powerful transformation.

AI Agents : From foundation to Multi - Agent Systems
English | March 23, 2026 | ASIN: B0GTMSJKY9 | 458 pages | Epub | 303.40 KB
This book focuses on next-generation AI systems, including AI agents, Model Context Protocol (MCP), and intelligent orchestration frameworks. AI agents have emerged not as incremental improvements to chatbots, but as a new computing paradigm. In just a few short years, they have evolved from research experiments to boardroom priorities. What makes this shift remarkable is not just the speed of adoption, but the depth of impact. Agents are not confined to customer service or IT automation; they are supporting clinical workflows, evaluating financial exposure, assisting legal research, managing infrastructure, and orchestrating enterprise operations. Let us learn more about it.

AI Agent Design Patterns: A Practical Guide to Reliable LLM Workflows, RAG Systems, Tool Use, Memory, Planning, and Intelligent Automation
English | May 19, 2026 | ASIN: B0H2BW3NXM | 292 pages | Epub | 359.85 KB
AI agents are changing how software works.We are moving from systems that simply respond to commands to systems that can reason, plan, use tools, retrieve knowledge, collaborate, critique their own work, and complete complex tasks with minimal supervision.But most people trying to build AI agents face the same problems:Their agents hallucinate.Their workflows break under complexity.Their prompts become messy and unmanageable.Their multi agent systems turn chaotic.Their tools fail unpredictably.Their memory systems become unreliable.And their "smart assistant" works well in demos but collapses in real-world use.This book was written to solve those problems.Agentic Design Patterns is a practical guide to designing reliable, scalable, and intelligent AI agent systems using modern agent architecture principles and proven design patterns.Instead of vague theory and hype, this book focuses on how real agent systems are structured, coordinated, controlled, and improved.Inside this book, you will learn:* What AI agents actually are and how they differ from chatbots and automation systems* How large language models reason, fail, hallucinate, and recover* The core building blocks behind modern agentic systems* How to design agents that can plan, retrieve information, use tools, reflect, critique, and collaborate* The difference between simple agents, router agents, planner agents, ReAct systems, and multi agent workflows* How specialist agents work together in coordinated systems* Why memory, context management, and retrieval systems matter* How to design safer and more reliable agent architectures* Common AI agent failure modes and how to avoid them* Practical implementation checklists for building production quality systems* Real architectural thinking behind modern AI agentsThis book is designed for:* AI engineers and developers* Product managers and technical founders* Automation builders* Software architects* Researchers and AI enthusiasts* Anyone who wants to understand how modern AI agent systems are actually designedWhether you are building AI assistants, research agents, coding systems, workflow automations, customer support agents, or multi agent orchestration systems, this book gives you the mental models and practical frameworks needed to build with confidence.The future of software is agentic.This book helps you understand how to design it properly.

AI AGENT MASTERY with Claude AI: A Practical Guide to Building Smart Systems That Plan, Reason, and Execute
English | May 12, 2026 | ASIN: B0GX2YDS3K | 167 pages | Epub | 1.07 MB
Build Smart Systems That Plan, Reason, and ExecuteThere is a fundamental difference between an application that answers questions and one that solves complex problems. Problem-solving requires understanding a broader goal, breaking it down into actionable sub-tasks, managing dependencies, and recovering gracefully when individual steps fail. AI Agent Mastery with Claude AI provides the definitive roadmap for engineering the architecture of deep reasoning.Written for intermediate to advanced Python developers, this guide demonstrates how to construct systems that formulate structured plans rather than simply generating prose. Every concept is backed by complete, tested code with no pseudocode or placeholder outputs, allowing you to implement production-grade logic immediately.Core Concepts Covered:Deep Reasoning Prompts: Design system prompts that force structured decomposition and Socratic questioning.Planning Systems: Build task tree generators, resolve dependencies, and manage topological execution ordering.Dynamic Re-Planning: Implement self-correction loops, checkpoint recovery, and failure detection mechanisms.Execution Engines: Develop code execution sandboxes, REST API orchestrators, and automated data pipelines.Mastery-Level Patterns: Deploy the Critic-Revise pattern and integrate Knowledge Graphs for structured analysis.Elevate your engineering skills and build autonomous applications capable of genuine, self-directed problem-solving.(Note: This is an independent publication and is not affiliated with or endorsed by Anthropic PBC).

AI + Mutual Funds for Smart Investors: How to Use ChatGPT, Gemini, Claude, and Free AI Tools to Build, Optimize, and Grow Your Mutual Fund Portfolio
English | June 10, 2026 | ASIN: B0GZHGFY9M | 227 pages | Epub | 1.57 MB

AGENTS ON THE EDGE: Building Autonomous AI Agents with Raspberry Pi
English | May 12, 2026 | ASIN: B0H1KBTJ5Q | 133 pages | Epub | 9.64 MB
What if a pocket-sized computer could think, see, hear, and act completely on its own?AI doesn't need the cloud. It doesn't need a supercomputer. It just needs the right guide and you've found it.Agents on the Edge takes you on a hands-on journey from zero to building fully autonomous AI agents on a Raspberry Pi. No engineering degree. No prior experience. Just curiosity and a willingness to build something extraordinary.You'll set up a lean, powerful agent operating system, run real language models locally, give your agent eyes with live computer vision, and teach it to make decisions all without sending a single byte to the internet. Then you'll go further: coordinating swarms of Pi nodes, adding voice interaction, locking down security, and exploring the future where IoT, robotics, and generative AI converge.This isn't a theory. Every chapter is a step closer to an agent that works privately, intelligently, and entirely in your hands.Edge intelligence is the next frontier. This is your invitation to build it.

AGENTIC AUDITOR: DEPLOYING AGENTIC AI ACROSS INTERNAL AUDIT
English | May 30, 2026 | ASIN: B0GY5WGS1N | 198 pages | Epub | 404.04 KB
Internal audit is entering the most consequential transformation in its modern history.A new generation of "agentic" AI systems can now pursue objectives, use tools, adapt to changing conditions, and execute complex work under human supervision. For U.S. banking audit functions, this changes more than technology. It changes how assurance itself is performed.In Agentic Auditor, veteran banking audit leader and regulatory-law-trained practitioner David Bakinsky provides a practical framework for deploying AI across internal audit while independently governing the institution's own AI systems.Drawing from real-world banking audit experience, the book shows how audit functions can evolve from periodic, sample-based testing toward continuous risk sensing, full-population testing, AI-assisted controls testing, and defensible governance over autonomous systems operating across credit, fraud, AML/KYC, compliance, and operations.More importantly, it addresses the profession's defining challenge:How can internal audit responsibly use AI while also providing independent assurance over it?Designed for Chief Audit Executives, audit leaders, risk professionals, compliance officers, regulators, and banking practitioners, Agentic Auditor delivers practical guidance rather than theoretical AI commentary.Inside, readers will find:A working playbook of ten AI audit agents across the audit lifecycleA practical first-ninety-days implementation roadmapSample audit programs and testing approachesGovernance frameworks aligned to emerging U.S. banking expectationsGuidance for preserving independence, evidence quality, and professional skepticism in AI-assisted engagementsWritten in clear, practical language for working audit professionals, Agentic Auditor is a field guide for leaders preparing their audit functions for the next era of banking oversight.

AGENTIC AI VOID: Hardening the Neural Frontier
English | May 12, 2026 | ASIN: B0H1H5B7C7 | 264 pages | Epub | 186.61 KB
The era of passive token prediction is dead. Welcome to the Loop.By 2026, the digital landscape has undergone a seismic shift. Large Language Models (LLMs) have evolved from static chat endpoints into Agentic World Models-recursive, stateful engines that don't just predict text; they simulate reality and execute actions across global infrastructure.AGENTIC AI VOID is the definitive technical manual for the new autonomous reality. It maps the expanded, fluid attack surface where "inputs" are now State Transitions and "vulnerabilities" are Semantic Drifts. This is the specialized handbook for the next generation of security architects tasked with defending the bridge between silicon reasoning and physical execution.Inside the Protocol:Recursive Zero-Day Defense: Neutralizing multi-step prompt injection in autonomous feedback loops.STI (Secure Tool Interface) Hardening: Architecting the layer where neural weights meet system APIs.State Hijacking & Poisoning: Defending the agent's memory from long-term vector-store corruption.Distributed Agent Governance: Securing inter-agent communication protocols (ACP 0.5) and shared latent spaces.World Model Integrity: Ensuring the agent's internal simulation doesn't diverge into a catastrophic "Hallucination Breach."This is not a book about prompts. It is a book about Power. Whether you are hardening a national power grid's autonomous core or securing the next evolution of personal agents, AGENTIC AI VOID provides the blueprints for resilience in the age of autonomy.Terminate the exploit. Secure the agent. Master the void.

ADVANCED LINEAR ALGEBRA FOR ARTIFICIAL INTELLIGENCE : The Mathematics Behind Machine Learning, Built One Decision at a Time
English | June 3, 2026 | ASIN: B0H34YLR5J | 299 pages | Epub | 15.26 MB
Master the Mathematics Behind Machine Learning, Data Science, and Intelligent SystemsLinear algebra is the foundation of modern artificial intelligence - but many textbooks are either too abstract or disconnected from real AI applications. Advanced Linear Algebra for AI bridges that gap by combining rigorous mathematics with practical intuition and machine learning relevance.Written for college students, engineers, data scientists, and AI learners, this book develops advanced linear algebra concepts step-by-step while showing how they power real-world AI and computational systems.Inside this book, you will explore: Vector spaces and subspaces through an AI-focused lens Matrices as linear transformations and computational tools Orthogonality, projections, and least-squares methods Eigenvalues, eigenvectors, and the Spectral Theorem Singular Value Decomposition (SVD) and matrix factorization Principal Component Analysis (PCA) for dimensionality reduction Matrix calculus and gradient-based optimization Numerical stability, conditioning, and computational accuracyMathematical foundations essential for machine learning and data scienceThis book is designed to move beyond memorization and help readers build genuine mathematical understanding. Clear explanations, worked examples, and AI-centered insights make advanced topics accessible without sacrificing rigor.Whether you are studying linear algebra for university coursework, preparing for graduate-level AI and machine learning, or strengthening your mathematical foundation for research and engineering, this book provides the tools and intuition needed to succeed.Ideal for:* College and university students* AI and machine learning learners* Engineering and STEM majors* Data science practitioners* Self-learners seeking deeper mathematical understandingIf you want to understand the mathematics that drives artificial intelligence - not just use the algorithms - Advanced Linear Algebra for AI is your roadmap.