Wydawca: K-i-s-publishing
Anjanava Biswas, Wrick Talukdar, Matthew R. Scott,...
Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, it explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks.Starting with the foundations of GenAI and agentic architectures, you’ll explore decision-making frameworks, self-improvement mechanisms, and adaptability. The book covers advanced design techniques, such as multi-step planning, tool integration, and the coordinator, worker, and delegator approach for scalable AI agents.Beyond design, it addresses critical aspects of trust, safety, and ethics, ensuring AI systems align with human values and operate transparently. Real-world applications illustrate how agentic AI transforms industries such as automation, finance, and healthcare. With deep insights into AI frameworks, prompt engineering, and multi-agent collaboration, this book equips you to build next-generation adaptive, scalable AI agents that go beyond simple task execution and act with minimal human intervention.*Email sign-up and proof of purchase required
Vasyl Zvarydchuk
Large language models can produce impressive demos, but turning them into reliable products takes more than better prompts. You need to understand model behavior, know when to use retrieval or fine-tuning, structure agents correctly, and evaluate systems before deployment.Building Agent-Powered Applications gives an end-to-end engineering perspective on creating production-ready generative AI solutions. Written by Microsoft Principal AI Engineer Vasyl Zvarydchuk, it helps software engineers, data scientists, and applied AI practitioners move from concept to implementation. You’ll begin with AI, NLP, embeddings, transformers, and LLM behavior, then progress to prompt engineering, summarization, classification, extraction, reasoning, RAG, and fine-tuning.The book shows how to design agentic workflows with tools, memory, planning, orchestration, and human-in-the-loop controls. You’ll learn to evaluate quality with offline and online testing, task-specific metrics, LLM-as-a-judge methods, and responsible AI checks. Rather than treating prompting, RAG, fine-tuning, and agents as separate topics, this book shows how they work together in practice. By the end, you’ll be able to make better architectural trade-offs, reduce failure modes, and build scalable, trustworthy AI applications.*Email sign-up and proof of purchase required
Henry Habib
Everyone’s talking about AI agents, but how do you build one that works in the real world? Not a toy demo, but an agent that solves real problems, saves time, and integrates into workflows. With vague frameworks, fragmented tooling, and endless hype, most developers are left without a clear path. The hardest part isn’t technical; it is knowing where to start.This book gives you that starting point. It’s a complete guide to building intelligent AI agents and agentic systems using the official OpenAI Agents SDK. It begins by grounding you in the core concepts, design principles, and architecture of AI agents, how they differ from other traditional systems, their advantages, and why that matters.Through practical step-by-step projects, you’ll master every feature of the SDK—tools, memory, RAG, multi-agent orchestration, tracing, handoffs, and more—while contributing to an end-to-end agent system that grows in complexity. Projects include a custom support agent, invoice and inventory assistant, health advisor, sales trainer, and data analyst, giving you production-ready skills.By the end, you’ll know how to design, build, and deploy agentic systems that interact with APIs, query databases, hand off to external systems, and drive meaningful outcomes. You won’t just understand AI agents; you’ll be ready to ship them.
Hanane Dupouy, Fayssal El Mofatiche
AI agents are rapidly changing how financial systems analyze information, make decisions, and automate complex workflows. While many resources explain agentic AI concepts, few show how to design and deploy AI agents that work reliably in finance. This book fills that gap.You will start by learning what AI agents are, how they differ from non-agentic systems, and when agentic architectures are appropriate. You’ll use Python with Claude and OpenAI models across hands-on labs covering design patterns, memory, agentic RAG, framework selection, reasoning paradigms such as ReAct, reflection, self-consistency, and Language Agent Tree Search, and multi-agent collaboration. You’ll then take a deep dive into financial use cases, including fundamental analysis, deep research, trading, insurance, and compliance, using technologies such as LangGraph, Claude Skills, the OpenAI Agents SDK, and LlamaIndex.You will learn to evaluate agent behavior, calibrate LLM judges, detect drift, and produce model risk reports. Finally, you will focus on operationalizing AI agents responsibly, covering the agent harness and execution loop, observability and tracing, deployment and versioning, guardrails, and governance with human oversight.By the end, you’ll be able to design, evaluate, and deploy financial AI agents that deliver real business value.*Email sign-up and proof of purchase required
Sif Baksh
Network troubleshooting is full of clues, but they are often buried in noisy alerts, long CLI output, missing topology context, and incomplete handoffs. Building AI Agents for Network Operations shows how to use AI agents, LLMs, and network automation in a controlled way, so engineers can get clearer evidence without giving up validation or operational control.You will start with local LLM workflows using Ollama and Python, then use a simple RACE prompt structure to make repeatable NetOps tasks clearer, safer, and easier to review. You will parse interface and BGP output into structured data, build a chatbot that keeps troubleshooting context, and connect the model to approved tools for device status, interfaces, reachability, topology, and BGP health. You will then build an agentic troubleshooting loop, package reusable network tools with MCP, and learn how to evaluate these workflows against logging, approvals, observability, runbooks, feature flags, and read-only pilot readiness.By the end of this book, you will have a practical path for turning AI ideas into NetOps workflows that can be tested in a lab, reviewed by your team, and adapted toward real-world network operations with the right controls.
Salvatore Raieli, Gabriele Iuculano
This book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving.Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples and real-world case studies reinforce each concept and show how the techniques fit together.By the end of this book, you’ll be able to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.*Email sign-up and proof of purchase required
Martin Yanev
Combining ChatGPT APIs with Python opens doors to building extraordinary AI applications. By leveraging these APIs, you can focus on the application logic and user experience, while ChatGPT’s robust NLP capabilities handle the intricacies of human-like text understanding and generation.This book is a guide for beginners to master the ChatGPT, Whisper, and DALL-E APIs by building ten innovative AI projects. These projects offer practical experience in integrating ChatGPT with frameworks and tools such as Flask, Django, Microsoft Office APIs, and PyQt.Throughout this book, you’ll get to grips with performing NLP tasks, building a ChatGPT clone, and creating an AI-driven code bug fixing SaaS application. You’ll also cover speech recognition, text-to-speech functionalities, language translation, and generation of email replies and PowerPoint presentations. This book teaches you how to fine-tune ChatGPT and generate AI art using DALL-E APIs, and then offers insights into selling your apps by integrating ChatGPT API with Stripe. With practical examples available on GitHub, the book gradually progresses from easy to advanced topics, cultivating the expertise required to develop, deploy, and monetize your own groundbreaking applications by harnessing the full potential of ChatGPT APIs.
Lucas A. Meyer
In the fast-paced world of AI, developers are constantly seeking efficient ways to integrate AI capabilities into their apps. Microsoft Semantic Kernel simplifies this process by using the GenAI features from Microsoft and OpenAI.Written by Lucas A. Meyer, a Principal Research Scientist in Microsoft’s AI for Good Lab, this book helps you get hands on with Semantic Kernel. It begins by introducing you to different generative AI services such as GPT-3.5 and GPT-4, demonstrating their integration with Semantic Kernel. You’ll then learn to craft prompt templates for reuse across various AI services and variables. Next, you’ll learn how to add functionality to Semantic Kernel by creating your own plugins. The second part of the book shows you how to combine multiple plugins to execute complex actions, and how to let Semantic Kernel use its own AI to solve complex problems by calling plugins, including the ones made by you. The book concludes by teaching you how to use vector databases to expand the memory of your AI services and how to help AI remember the context of earlier requests. You’ll also be guided through several real-world examples of applications, such as RAG and custom GPT agents.By the end of this book, you'll have gained the knowledge you need to start using Semantic Kernel to add AI capabilities to your applications.