[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-115755-en":3,"doc-seo-115755-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},115755,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Systems Thinking for Agentic AI - A Software Architect’s Guide to Building Reliable LLM and Agent Systems","Systems Thinking for Agentic AI explains how to translate core software engineering practices into production-grade AI systems driven by large language models. The book connects prompt design, retrieval-augmented generation, tool use, memory, evaluation, and observability into a coherent architecture. It guides engineers from prompt experiments toward systems that can be reasoned about, measured, debugged, operated, and continuously improved through reliable agent workflows and controlled execution.","Systems Thinking forAgenticAI  \nA Software Architect’s Guide to Building Reliable LLM and Agent Systems  \nEdiz Najim  \n[OceanofPDF.com](OceanofPDF.com)  \nCopyright  \nSystems Thinking forAgenticAI  \nCopyright © 2026 Ediz NAJIM. All rights reserved.  \nNo part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic or mechanical, without prior written permission of the copyright holder, except for brief quotations used in reviews, criticism, or scholarly discussion.  \nFirst edition.  \nISBN: 9798197120960  \nPublication date: 2026  \nPublisher: Ediz NAJIM  \n[OceanofPDF.com](OceanofPDF.com)  \nBook Promise  \nSoftware engineers and architects already know how to build real systems: APIs, services, databases, queues, caches,latency budgets, failure handling, security, and production trade-offs.  \nThis book shows how to extend that engineering mindset into AI systems powered by large language models. It explains how prompts, retrieval, tools, memory, evaluation, observability, and agent workflows fit together inside production-minded software architecture. The goal is simple: move from prompt experiments to AI systems you can reason about, measure, debug, operate, and improve.  \n[OceanofPDF.com](OceanofPDF.com)  \nContents  \nPreface  \nAcknowledgments  \nHow to Read This Book  \nFoundations  \nFrom AI to Agent Systems  \n1.1 Artificial Intelligence as a Broad Field  \n1.2 From Rules to Learning Systems  \n1.3 Machine, Deep Learning, and Generative AI  \n1.4 The Role of LLMs  \n1.5 Why the LLM Is Only One Component  \n1.6 Retrieval-Augmented Generation  \n1.7 From Language Generation to Action  \n1.8 What an AIAgent Is  \n1.9 LLM, RAG, Tool Use, and Agents  \n1.10 Why Engineers Need a System View  \n1.11 Where We Go From Here  \n1.12 The Architecture Mindset  \n1.13 From AI Skills to AI System Capabilities  \nSummary  \nLLM Internals and Output Generation  \n2.1 Tokens and Tokenization  \n2.2 Embeddings  \n2.3 Sentence Embeddings  \n2.4 Transformer Architecture  \n2.5 The Attention Mechanism  \n2.6 Self-Attention  \n2.7 Context Window  \n2.8 From Internal Processing to Output Generation  \n2.9 Next-Token Prediction  \n2.10 Probability Distribution Over Tokens  \n2.11 Temperature and Sampling  \n2.12 Top-k Sampling  \n2.13 Top-p Sampling  \n2.14 Deterministic vs Probabilistic Output  \n2.15 Output Variability in LLMs  \n2.16 Causes of Hallucination  \n2.17 Hallucination Mitigation Techniques  \n2.18 Selecting Generation Strategies Summary  \nPrompt Engineering in LLM Systems  \n3.1 Prompt Definition and Role  \n3.2 System Prompts  \n3.3 System Prompt Impact  \n3.4 Few-Shot Prompting  \n3.5 Zero-Shot, One-Shot, and Few-Shot  \n3.6 Chain-of-Thought Prompting  \n3.7 Structured Output  \n3.8 Structured Output in Systems  \n3.9 Prompt Versioning  \n3.10 Prompt Versioning in Practice  \n3.11 Prompting as a System Layer  \n3.12 Common Prompting Mistakes  \n3.13 Prompt Safety and Injection Risks  \n3.14 Prompt Evaluation  \n3.15 Prompt and Decoding Interaction  \n3.16 Prompt Size and Token Budgeting LLM Engineering  \nLLMs as System Components  \n4.1 The LLM Inside the Application Boundary  \n4.2 Function Calling  \n4.3 Function Calling as a Controlled Contract  \n4.4 Tool Calling Extends the System Boundary  \n4.5 The Tool Calling Workflow  \n4.6 When Tool Use Starts to Look Like an Agent  \n4.7 LLM Evaluation  \n4.8 Evaluation in Practice  \n4.9 Types of Evaluation  \n4.10 Evaluation Prevents Silent Regressions  \n4.11 LLM Observability  \nRAG Systems (Retrieval-Augmented Generation)  \n5.1 RAG Concepts and Architecture  \n5.2 Embedding Models  \n5.3 Vector Databases  \n5.4 Similarity Search  \n5.5 Cosine Similarity  \n5.6 Chunking Strategies  \n5.7 Hybrid Search  \n5.8 Hybrid Search Benefits  \n5.9 Metadata Filtering  \n5.10 Retrieval Quality Shapes Answers  \n5.11 Common RAG Failure Modes  \n5.12 Context Assembly and Context Engineering  \n5.13 Token Budgeting  \n5.14 RAG Latency, Performance, and Caching  \n5.15 Advanced RAG: From Retrieval to Navigation Ag","cbCaioWAdFIFVRoF","https://ap.wps.com/l/cbCaioWAdFIFVRoF","pdf",28282090,7,1,384,"English","en",105,"# Preface\n## How to Read This Book\n# Foundations\n## From AI to Agent Systems\n# LLM Internals and Output Generation\n## Prompt Engineering in LLM Systems\n# LLMs as System Components\n## RAG Systems (Retrieval-Augmented Generation)\n# Tool Orchestration and MCP\n## Tool Orchestration\n# Agent Runtime and Execution\n## Planning in Agent Systems\n# Memory in Agent Systems\n## Types of Memory","[{\"question\":\"What problem does the book address in building LLM and agent systems?\",\"answer\":\"It addresses the gap between isolated prompt experiments and production-minded AI systems that are measurable, debuggable, observable, and continuously improvable.\"},{\"question\":\"Which core system components does the book cover?\",\"answer\":\"It covers prompts, retrieval-augmented generation (RAG), tools and tool orchestration, memory, evaluation, observability, and agent workflows inside software architecture.\"},{\"question\":\"Why does the book recommend a system view for engineers working with LLMs?\",\"answer\":\"Because LLM behavior depends on multiple interacting components, and reliable results require designing boundaries, execution flow, feedback loops, and evaluation to avoid silent regressions.\"}]","Systems Thinking for Agentic AI - 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