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Enterprise Agent Platform Engineering
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Enterprise Agent Platform Engineering
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Preface
Acknowledgements
Front Matter Guide
Contributors
Part I Overview and Platform Perspective
Part I Overview and Platform Perspective
Chapter 1 The Essence of Agents
Chapter 2 Enterprise Agent Platform Boundaries
Chapter 3 AI-Native Business Systems
Chapter 4 Full-Book Map
Part II Models and Inference
Part II Models and Inference
Chapter 5 LLM Selection
Chapter 6 Local Inference Engines
Chapter 7 Inference Optimization
Chapter 8 Structured Output and Prompt Engineering
Chapter 9 Model Customization and Knowledge Enhancement
Part III Data Infrastructure
Part III Data Infrastructure
Chapter 10 Data Ingestion and Integration
Chapter 11 Data Lake and Lakehouse
Chapter 12 Lakehouse Engines and OLAP
Chapter 13 Streaming and Real-Time Data
Chapter 14 Data Orchestration and Quality
Chapter 15 Metadata, Lineage, Contracts, and Metrics
Part IV Vectors, Retrieval, and Knowledge Engineering
Part IV Vectors, Retrieval, and Knowledge Engineering
Chapter 16 Embedding Models
Chapter 17 Embedding Fine-tuning and Reranking
Chapter 18 Vector Databases and Index Algorithms
Chapter 19 Document Parsing and Multimodal OCR
Chapter 20 RAG Engineering and Advanced Retrieval
Chapter 21 Knowledge Engineering
Part V Agent Capabilities
Part V Agent Capabilities
Chapter 22 Agent Runtime
Chapter 23 Tool Registry and Function Calling
Chapter 24 MCP and Enterprise Tooling
Chapter 25 Planner and Orchestration Patterns
Chapter 26 Agentic Workflow
Chapter 27 Memory System
Chapter 28 Multi-Agent Collaboration
Chapter 29 Agent Protocols and Standards
Chapter 30 Human-in-the-loop and Long-Running Tasks
Chapter 31 Framework Comparison
Part VI DataAgent Deep Dive
Part VI DataAgent Deep Dive
Chapter 32 DataAgent Product Forms
Chapter 33 Semantic Layer Engineering
Chapter 34 NL2SQL Engineering
Chapter 35 Text-to-Pandas and Text-to-Python
Chapter 36 Data Analysis, Visualization, and Reporting
Chapter 37 DataAgent Benchmarking and Ecosystem
Part VII Observability, Evaluation, and Cost
Part VII Observability, Evaluation, and Cost
Chapter 38 Observability and Trace
Chapter 39 Offline Evaluation and Benchmarks
Chapter 40 Online Evaluation and LLM-as-Judge
Chapter 41 Cost Governance and Cache Optimization
Chapter 42 SLO, Rate Limiting, and Degradation
Part VIII Deployment and Infrastructure
Part VIII Deployment and Infrastructure
Chapter 43 GPU Scheduling and Kubernetes
Chapter 44 Model Deployment
Chapter 45 LLM Gateway and Multi-Tenancy
Chapter 46 GitOps, IaC, and Edge Inference
Part IX Frontend, Interaction, and Multimodality
Part IX Frontend, Interaction, and Multimodality
Chapter 47 Conversational UI and Streaming Output
Chapter 48 Generative UI and Rich Interaction
Chapter 49 Multimodal Input and Voice Agents
Part X Security, Compliance, and Organization
Part X Security, Compliance, and Organization
Chapter 50 Security and Offense-Defense
Chapter 51 Guardrails and Content Safety
Chapter 52 Compliance and Regulation
Chapter 53 Organization, Talent, and Platform Evolution
Part XI Case Methodology
Part XI Case Methodology
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