Scalable Ai Agent Engineering
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Scalable Ai Agent Engineering
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Author : NEWMAN. CHANDLER
language : en
Publisher: Independently Published
Release Date : 2025-07-14
Scalable Ai Agent Engineering written by NEWMAN. CHANDLER and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-14 with Computers categories.
Scalable AI Agent Engineering: Extend Context Windows and Implement Reliable Memory Systems with Semantic Kernel and Modern Vector Stores Struggling to build AI agents that retain crucial context as conversations grow and data volumes explode? You're not alone-developers everywhere face the same hurdle: context windows that choke, memory systems that buckle, and costly workarounds that never scale. Scalable AI Agent Engineering delivers a hands-on, code-first blueprint to conquer these challenges using Microsoft's Semantic Kernel and today's leading vector stores. You'll learn how to stretch context windows beyond their limits and design memory architectures that are both reliable and cost-effective-so your AI agents stay sharp, coherent, and lightning-fast. Inside, you'll discover how to: Initialize and customize Semantic Kernel in Python and .NET for seamless agent development Construct layered memory (short-term buffers, vector indexes, long-term archives) that balances speed with depth Integrate modern vector stores-FAISS, Pinecone, Qdrant, Redis-for blazing-fast semantic search and retrieval Implement RAG pipelines that ground your agents' answers in real data, slashing hallucinations Automate context management with sliding-window buffers, summarization cascades, and auto-compression routines Orchestrate multi-agent workflows that share memory, coordinate tasks, and handle complex pipelines from document ingestion to invoice generation Deploy and scale on Kubernetes with autoscaling, telemetry, structured logging, and robust monitoring Benchmark cost vs. performance across embeddings and LLM models to optimize every dollar you spend By the final page, you'll wield a production-ready toolkit for building AI companions that remember everything-and forget nothing that matters. Whether you're crafting chatbots, research assistants, or Copilot-style integrations, this book gives you the patterns and code to ship scalable, reliable agents today. Ready to transform your AI development and outpace the competition? Secure your copy of Scalable AI Agent Engineering now-and start engineering agents that truly scale.
Practical Ai Agent Engineering
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Author : Luther C Hansen
language : en
Publisher: Independently Published
Release Date : 2025-07-19
Practical Ai Agent Engineering written by Luther C Hansen and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-19 with Computers categories.
Practical AI Agent Engineering: Cloud, Edge, and Multi-Agent Strategies for Real-World Scalability Facing the challenge of building scalable, reliable AI agent systems that perform seamlessly across cloud and edge environments? Practical AI Agent Engineering: Cloud, Edge, and Multi-Agent Strategies for Real-World Scalability offers the definitive guide for developers and engineers ready to master the architecture and deployment of intelligent agents in complex, production settings. This book presents a clear, hands-on approach to designing, orchestrating, and managing AI agents that work cohesively across distributed infrastructures. Learn how to build modular, plug-and-play agents, implement multi-agent coordination protocols, and leverage cloud and edge technologies for efficient deployment. Security, compliance, continuous monitoring, and automated upgrades are integrated into practical workflows to ensure your systems remain robust and future-ready. Readers will gain expertise in: Architecting modular, scalable AI agents suitable for diverse environments Applying containerization and orchestration tools like Docker and Kubernetes for deployment Implementing secure API access and fine-grained permission models Managing multi-agent communication with event-driven and Agent2Agent protocols Designing continuous integration and over-the-air update pipelines for edge devices Embedding AI agents into organizational workflows with human-in-the-loop strategies Navigating compliance requirements and ethical considerations in enterprise AI Are you ready to elevate your AI projects from prototypes to production-grade systems? This book equips you with the practical skills and architectural insights necessary to build intelligent agent ecosystems that scale efficiently, operate securely, and adapt to evolving business demands. Whether you are an AI engineer, system architect, or DevOps professional, Practical AI Agent Engineering provides the structured guidance and expert knowledge to confidently engineer the next generation of AI-driven solutions. Equip yourself with the tools to deliver AI agents that perform reliably in the real world-start transforming your AI development process today.
Pytorch Ai Agent Engineering
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Author : Jerry Caraballo
language : en
Publisher: Independently Published
Release Date : 2025-06-29
Pytorch Ai Agent Engineering written by Jerry Caraballo and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-29 with Computers categories.
PyTorch AI Agent Engineering: Build Intelligent, Scalable Machine Learning Agents with Real-World Python Workflows and Modern Deep Learning Tools PyTorch AI Agent Engineering is your essential guide to building robust, production-ready machine learning agents using modern Python workflows and cutting-edge deep learning tools. This hands-on book cuts through the noise and gives you practical, actionable strategies for every stage of agent development - from concept to deployment. What sets this book apart? End-to-End Engineering: Master every step, from designing minimal QA agents and building data pipelines, to packaging and deploying models with TorchScript, ONNX, and FastAPI. Practical Agent Architecture: Learn how to construct modular, scalable agent systems using PyTorch, Lightning, Hydra, and the latest open-source frameworks. Real-World Applications: Integrate classic ML with modern deep learning, deploy GANs for data augmentation, and orchestrate multi-agent pipelines for coding assistants, QA bots, and more. Production-Ready Focus: Apply proven techniques for model versioning, reproducibility, security compliance (GDPR/CCPA), and automated CI/CD. Performance and Reliability: Profile speed and memory, test for accuracy, and scale horizontally on Kubernetes - so your agents never hit a wall when it matters most. Are you a developer, ML engineer, or data scientist eager to move beyond one-off demos? Do you want workflows that scale, adapt, and deliver results in real environments? This book is for you.
Ai Agent Memory Context Engineering
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Author : Peter E Poisson
language : en
Publisher: Independently Published
Release Date : 2025-09-08
Ai Agent Memory Context Engineering written by Peter E Poisson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-08 with Computers categories.
AI Agent Memory & Context Engineering: A Practical Guide to Building Smarter, Personalized, and Scalable AI Agents with LangChain, RAG, and Vector Databases Discover how to give AI agents the power of memory and context unlocking smarter, more adaptive, and truly personalized systems. Artificial intelligence has made huge leaps in language understanding, but without memory and context, most agents remain shallow and repetitive. This book shows you how to bridge that gap by engineering AI agents that remember, adapt, and scale with real-world applications. From designing context-aware interactions to implementing retrieval-augmented generation (RAG) and vector databases, you'll learn the technical and design strategies needed to build systems that go beyond one-off responses. With hands-on examples, case studies, and best practices, this guide equips practitioners and product builders to move from theory to production-ready solutions. Whether you're developing customer support bots, healthcare assistants, or fintech agents, this book provides the roadmap for aligning memory design with business goals while ensuring privacy, reliability, and compliance. Benefits: Practical guidance on integrating LangChain, vector databases, and RAG into AI workflows. Case studies across industries including customer service, healthcare, and fintech. Techniques for balancing accuracy, efficiency, and personalization in memory-powered agents. Insights on ethical, regulatory, and transparency challenges in memory design. Future-focused exploration of trends in decentralized and collaborative memory models. If you're ready to build the next generation of intelligent, memory-powered AI agents, get your copy today and start transforming your ideas into reality.
Building Agentic Ai Systems
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Author : Anjanava Biswas
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-04-21
Building Agentic Ai Systems written by Anjanava Biswas and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-21 with Computers categories.
Master the art of building AI agents with large language models using the coordinator, worker, and delegator approach for orchestrating complex AI systems Free with your book: PDF Copy, AI Assistant, and Next-Gen Reader Key Features Understand the foundations and advanced techniques of building intelligent, autonomous AI agents Learn advanced techniques for reflection, introspection, tool use, planning, and collaboration in agentic systems Explore crucial aspects of trust, safety, and ethics in AI agent development and applications Book DescriptionGain 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.What you will learn Master the core principles of GenAI and agentic systems Understand how AI agents operate, reason, and adapt in dynamic environments Enable AI agents to analyze their own actions and improvise Implement systems where AI agents can leverage external tools and plan complex tasks Apply methods to enhance transparency, accountability, and reliability in AI Explore real-world implementations of AI agents across industries Who this book is for This book is ideal for AI developers, machine learning engineers, and software architects who want to advance their skills in building intelligent, autonomous agents. It's perfect for professionals with a strong foundation in machine learning and programming, particularly those familiar with Python and large language models. While prior experience with generative AI is beneficial, the book covers foundational concepts for those new to agentic systems.
Next Generation Ai Agent Engineering
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Author : Michael J Crocker
language : en
Publisher: Independently Published
Release Date : 2025-10-07
Next Generation Ai Agent Engineering written by Michael J Crocker and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-07 with Computers categories.
What if machines could think, reason, and make decisions with a sense of awareness similar to human logic? What if the systems you build could not only process information but also understand it, adapt to new situations, and improve continuously without manual intervention? Next-Generation AI Agent Engineering takes you into that reality. This book is written for innovators, engineers, and curious minds who want to move beyond traditional automation and explore the next era of intelligent, autonomous systems. It's not about futuristic speculation - it's about practical, scalable design principles that are shaping the technology of today and tomorrow. Have you ever wondered how digital agents manage memory, understand context, or coordinate across multiple systems without breaking efficiency? Here, every chapter walks you through those hidden mechanisms - from the fundamentals of scalable architecture to the secrets behind reliable learning loops, semantic processing, and structured communication between agents. Through real-world examples, modular system breakdowns, and hands-on explanations, this book shows you how to build AI agents that think systematically, reason with precision, and act with autonomy. You'll explore how context expansion works, how memory persistence is maintained, and how vector-based reasoning and structured knowledge graphs create a new foundation for adaptive intelligence. But this isn't just another technical manual. It's written like a conversation - a deep exchange of ideas where each topic challenges your current understanding of what machines can do. You'll be asked questions that encourage critical thinking: What defines intelligence in an engineered system? How can context windows be expanded without losing relevance? What does it mean to design memory that never forgets, yet never clutters? And how can agents collaborate securely, ethically, and transparently? By the time you reach the final chapters, you'll not only understand the architecture of next-generation agentic systems - you'll be equipped to design, build, and optimize them. This book connects theory with engineering practice, guiding you step by step through the frameworks that make AI agents scalable, trustworthy, and aligned with real-world performance goals. If you've been searching for a resource that speaks to both your technical curiosity and your creative vision, Next-Generation AI Agent Engineering is that bridge. It's built for those who refuse to settle for surface-level understanding and want to craft systems that learn, adapt, and evolve intelligently. Are you ready to engineer the next generation of intelligent agents? Open these pages - your future in AI design begins here.
Building Agentic Ai With Rust
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Author : Evan Sterling
language : en
Publisher: Independently Published
Release Date : 2025-06-10
Building Agentic Ai With Rust written by Evan Sterling and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-10 with Computers categories.
Agentic AI-autonomous systems capable of perception, reasoning, and action-is redefining how we build intelligent applications. From AI customer service agents and healthcare assistants to real-time financial analysis tools, these systems integrate Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and goal-oriented control. Rust, with its unmatched performance, safety guarantees, and asynchronous power via Tokio, is the ideal language to build scalable, high-concurrency AI agents that are production-ready. Written by a seasoned systems engineer and AI practitioner, Building Agentic AI with Rust is the first comprehensive guide focused on using Rust to build high-performance, autonomous AI agents. With deep real-world experience, clean architectural patterns, and a practical teaching style, this book bridges the gap between cutting-edge AI research and robust, deployable software engineering practices. This hands-on guide shows developers how to architect, implement, and deploy agentic AI systems using Rust and modern AI tools like OpenAI, Hugging Face, and vector search engines. Each chapter provides a step-by-step approach, from designing the agent loop to implementing a scalable RAG system and deploying with Docker and cloud services. You'll learn best practices for async programming with Tokio, profiling for performance, and implementing real-world use cases across industries. Implementing the Perceive-Reason-Act loop in Rust Architecting modular AI agents with traits and async tasks Integrating OpenAI and Hugging Face LLMs using structured prompts Building Retrieval-Augmented Generation (RAG) pipelines Scaling with Tokio, caching, and vector stores like Qdrant Packaging, containerizing, and deploying agents to AWS and GCP Monitoring, logging, and optimizing agents for production Full case studies: customer support, healthcare, and financial AI This book is written for Rust developers, AI engineers, system architects, and technical enthusiasts looking to build powerful autonomous agents with real-world capabilities. If you're comfortable with Rust and want to extend your skills into modern AI systems, this guide is for you. No prior experience with LLMs or RAG is required-concepts are introduced clearly and practically. Agentic AI is no longer experimental-it's production-ready, and it's here now. As the field of generative AI evolves rapidly, learning to build scalable, secure, and performant agents with Rust puts you ahead of the curve. Don't wait to catch up with the future-become one of the first engineers building it. Master the intersection of systems programming and generative AI. Build fast, safe, and intelligent autonomous agents with Rust today. Get your copy of Building Agentic AI with Rust and start coding the future of AI, now.
Ultimate Agentic Ai With Autogen For Enterprise Automation
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Author : Shekhar Agrawal
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2025-06-30
Ultimate Agentic Ai With Autogen For Enterprise Automation written by Shekhar Agrawal and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-30 with Computers categories.
TAGLINE Empowering Enterprises with Scalable, Intelligent AI Agents. KEY FEATURES ● Hands-on practical guidance with step-by-step tutorials and real-world examples. ● Build and deploy enterprise-grade LLM agents using the AutoGen framework. ● Optimize, scale, secure, and maintain AI agents in real-world business settings. DESCRIPTION In an era where artificial intelligence is transforming enterprises, Large Language Models (LLMs) are unlocking new frontiers in automation, augmentation, and intelligent decision-making. Ultimate Agentic AI with AutoGen for Enterprise Automation bridges the gap between foundational AI concepts and hands-on implementation, empowering professionals to build scalable and intelligent enterprise agents. The book begins with the core principles of LLM agents and gradually moves into advanced topics such as agent architecture, tool integration, memory systems, and context awareness. Readers will learn how to design task-specific agents, apply ethical and security guardrails, and operationalize them using the powerful AutoGen framework. Each chapter includes practical examples—from customer support to internal process automation—ensuring concepts are actionable in real-world settings. By the end of this book, you will have a comprehensive understanding of how to design, develop, deploy, and maintain LLM-powered agents tailored for enterprise needs. Whether you're a developer, data scientist, or enterprise architect, this guide offers a structured path to transform intelligent agent concepts into production-ready solutions. Start building the next generation of enterprise AI agents with AutoGen—today. WHAT WILL YOU LEARN ● Design and implement intelligent LLM agents using the AutoGen framework. ● Integrate external tools and APIs to enhance agent functionality. ● Fine-tune agent behavior for enterprise-specific use cases and goals. ● Deploy secure, scalable AI agents in real-world production environments. ● Monitor, evaluate, and maintain agents with robust operational strategies. ● Automate complex business workflows using enterprise-grade AI solutions. WHO IS THIS BOOK FOR? This book is tailored for AI/ML engineers, software developers, data scientists, solution architects, enterprise tech leads, product managers, innovation strategists, and CTOs. It’s also valuable for business leaders and decision-makers seeking to understand and leverage LLM-powered agentic systems for scalable, intelligent enterprise solutions. TABLE OF CONTENTS 1. Introduction to LLM Agents (Foundation and Impact) 2. Architecting LLM Agents (Patterns and Frameworks) 3. Building a Task-Oriented Agent using AutoGen 4. Integrating Tools for Enhanced Functionality 5. Context Awareness and Memory System 6. Designing Multi-Agent Systems 7. Evaluation Framework for Agents and Tools 8. Agent-Security, Guardrails, Trust, and Privacy 9. LLM Agents in Production 10. Use Cases for Enterprise LLM Agents 11. Advanced Prompt Engineering for Effective Agents Index
Practical Ai Engineering
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Author : Juno Darian
language : en
Publisher: Independently Published
Release Date : 2025-07-25
Practical Ai Engineering written by Juno Darian and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-25 with Computers categories.
Build, Deploy, and Scale Real-World AI Systems-From Foundation Models to Full-Stack Production Pipelines Are you ready to move beyond tutorials and toy models into the real world of scalable, production-ready AI? Practical AI Engineering is your complete, no-fluff, hands-on guide to building modern AI applications from scratch to mastery. Whether you're aiming to become a full-stack AI engineer, deploy cutting-edge LLMs (Large Language Models), or bring real-time Retrieval-Augmented Generation (RAG) systems into production, this book takes you there-step by step. Written for engineers, ML practitioners, and developers who want more than just theoretical knowledge, this book equips you with battle-tested workflows, system design patterns, and toolchains used by top AI teams. What You'll Master Inside This Book: AI Engineering from the Ground Up - Learn what AI engineering really means: beyond models, into systems - Master the end-to-end AI lifecycle (Design → Deploy → Maintain) - Think like a systems engineer for real-world impact The Full Toolkit for Modern AI Engineers - Python patterns, TensorFlow vs. PyTorch, FastAPI, HuggingFace, LangChain - Data pipelines, Docker, Kubernetes, and GitOps workflows - Experiment tracking, versioning, and CI/CD automation LLMs, Transformers, and Prompt Engineering in Practice - Understand how GPT models work and scale - Use OpenAI APIs and HuggingFace models efficiently - Apply few-shot, chain-of-thought, and retrieval-augmented strategies - Implement LLMOps for inference, caching, and cost control Retrieval-Augmented Generation (RAG) and GraphRAG - Chunking, embeddings, and vector databases (FAISS, Pinecone, Qdrant) - Build RAG systems with LangChain, FastAPI, and custom memory - Go beyond text: create knowledge-augmented LLMs with Neo4j and GraphRAG - Complete projects: Legal QA bots, research assistants, scalable chatbots Agentic AI and Multi-Tool Orchestration - Build agents that use tools like Web Browsing, SQL, and PDFs - Explore LangChain Agents, OpenAgents, AutoGen frameworks - Monitor hallucinations, plan actions, and design recovery flows - Ensure safety, logging, and performance in agentic systems Production-Ready Deployment with Docker & Kubernetes - Package LLMs and APIs into portable containers - Use docker-compose and Helm charts for orchestration - Deploy scalable clusters with GPU access and autoscaling - Implement health probes, registries, and versioned microservices Observability, Evaluation & Continuous Delivery - Monitor LLM drift, RAG relevance, and real-time model metrics - Run A/B tests, feedback loops, and prompt re-ranking - Automate your ML pipelines using GitHub Actions + MLflow - Set up failover, alerts, and canary deployments Ethical and Global AI Deployment - Handle bias, safety, privacy, and data sovereignty - Harden APIs against adversarial prompts and jailbreaking - Deploy inclusive systems across global and non-Western contexts Among others.. BONUS: Companion Project Repositories + Cheat Sheets Real projects: RAG chatbots, GraphRAG assistants, LLM agents If you're looking for a deeply practical, industry-relevant, and project-driven book to help you master modern AI engineering-this is it. Perfect for: - AI/ML engineers and full-stack developers - Backend engineers diving into LLMs and RAG - Technical founders building AI-powered products Join the future of AI development - become a practical AI Engineer.
Build Ai Agents With Python
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Author : Eidan Crest
language : en
Publisher: Independently Published
Release Date : 2025-11-17
Build Ai Agents With Python written by Eidan Crest and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-17 with Computers categories.
AI has changed-but tutorials haven't. Most guides stop at simple chatbots and ignore what developers really need today: agentic AI Python skills-how to design, build, and ship intelligent systems that plan, act, call tools, and integrate with real-world workflows. If you're a developer trying to build AI agents that go beyond text replies and actually perform tasks, you've probably felt the gap: fragmented resources, partial examples, and no clear path from prototype to production. This book closes that gap. It is your complete, end-to-end AI agent orchestration and engineering playbook-focused on real-world Python automation AI and modern frameworks. If you want to become the developer who can design, implement, and ship serious AI systems, this is the guide you've been looking for. With this book, you will: Master the core patterns of agent engineering - reasoning loops, planning, memory, tools, safety, and governance, all implemented in clean, production-ready agentic AI Python code. Build practical, real-world projects - from single agents to complex multi agent systems Python architectures that collaborate, debate, and delegate work. Learn LangGraph the right way - with a hands-on, step-by-step LangGraph tutorial that shows you how to model nodes, edges, and state for research, coding, and document workflows. Use Autogen in real scenarios - not just basic demos, but a full Autogen Python guide that walks you through interdependent agents, conversational loops, human-in-the-loop approvals, and safe execution. Turn your data into intelligence with LlamaIndex - build robust LlamaIndex RAG agents that index documents, perform retrieval-augmented generation, and plug cleanly into your wider agent ecosystem. Engineer powerful Python tool-calling agents - design and implement Python tool calling agents that interact with APIs, databases, files, browsers, and OS-level tools using structured, validated functions. Automate real workflows, not just prompts - build full AI workflow automation systems that handle research, reporting, coding, web tasks, and business operations from end to end. Deploy with confidence - apply best practices for testing, evaluation, monitoring, and AI agent orchestration using FastAPI, Docker, serverless platforms, and observability tooling. Whether you're a software engineer, data scientist, ML engineer, indie hacker, or technical founder, this book gives you the skills and patterns to move beyond simple scripts and into robust, scalable Python automation AI systems that deliver real value.