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From Industrial Software toAI AgentsAGENTICSense · Reason · Act · EvolveFrom Industrial Software to AI Agents, AGENTIC: Sense, Reason, Act, Evolve

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Front Matter

About the Author

Author's Preface

How to Read This Book

Contents

Part I · Foundations: From Industrial Software to the IoT Platform Base

Chapter 1. IoT Overview: From Industrial Software to AI Agents

1.1 The Evolution and Limits of Industrial Software

1.2 The Informatization Wave: The Legacy of Three Network Revolutions

1.3 The Definition and Essential Elements of the Internet of Things

1.4 Evolution and the Current State of the Industry

1.5 The Paradigm Shock of Large AI Models

1.6 AIoT: From Addition to Restructuring

1.7 Engineering Wrap-Up and Practical Guidance

Chapter 2. IoT System Architecture

2.1 From the Classic Four Layers to a New Architecture for the AI Era

2.2 The Shift to the Data Loop

2.3 IoT DC3 Microservice Architecture in Practice

2.4 Architecture Takeaways and Extensions

Chapter 3. Sensing-Layer Fundamentals

3.1 Sensing Layer Overview

3.2 Sensor Technology

3.3 RFID Technology

3.4 Positioning Technologies

3.5 Edge Computing Nodes

3.6 On-Device AI and Adaptive Sampling

3.7 Thing Model and Device Abstraction

3.8 Sensing-Layer Engineering Summary

Chapter 4. Network-Layer Communication Technologies

4.1 Overview of Mainstream IoT Communication Technologies

4.2 The Challenge of Protocol Fragmentation and the Need for Unified Access

4.3 Design Principles of the Unified Access Layer

4.4 IoT DC3's Driver-Module Architecture and Driver SDK

4.5 Engineering Case Study: A Multi-Protocol Gateway for Unified Access

4.6 Chapter Wrap-Up

Chapter 5. Platform Layer and Data Processing

5.1 Overall Architecture and Core Components of the Platform Layer

5.2 The Data Path from Device to Cloud

5.3 Collaboration Between Edge Computing and Cloud Computing

5.4 Data Storage and Efficient Querying

5.5 AI-Driven Intelligent Data Processing (Concept Introduction)

5.6 Case Study and Deployment Checks

Part II · Technology: Cloud-Native and AI-Agent Capabilities

Chapter 6. IoT Software Development

6.1 IoT Development Languages and Communication Protocols

6.2 Microservice Architecture Methodology

6.3 IoT DC3 Engineering Practice

6.4 Engineering Summary and Further Reading

Chapter 7. AIoT and Agent Applications

7.1 The AIoT Technology Landscape and Evolution

7.2 Spring AI and IoT Integration

7.3 The IoT DC3 Agentic Center in Practice

7.4 Multi-Model Support and Private Deployment

7.5 From Copilot to Agent: The Autonomy Progression of IoT Operations

7.6 Before Going Live: Practice Checklist and Common Pitfalls

Chapter 8. IoT Security

8.1 An Overview of IoT Security

8.2 Device Security and Authentication

8.3 Communication Security

8.4 Data Security and Privacy Protection

8.5 Security Challenges in the AI Era

8.6 Security Engineering Practices

8.7 Engineering Wrap-Up

Chapter 9. IoT Protocols and Standards

9.1 Overview of IoT Application-Layer Protocols

9.2 The MQTT Protocol in Depth

9.3 CoAP and LwM2M Protocols

9.4 HTTP/HTTPS and BLE GATT Interoperability

9.5 The MCP Protocol: A Bridge Between AI and IoT

9.6 From Protocol Adaptation to Semantic Interoperability

9.7 Engineering Wrap-Up and Practical Checklist

Part III · Applications: Multi-Scenario Practice and Trusted Collaboration

Chapter 10. Industrial IoT and Smart Manufacturing

10.1 Industry 4.0 and Digital Twins

10.2 Industrial IoT Data Acquisition: Modbus and OPC UA

10.3 Industrial Time-Series Data and Rule Engines

10.4 Predictive Maintenance and the AI Closed Loop

10.5 IoT DC3 in Industrial Practice: Case Studies

10.6 Chapter Review and Deployment Checklist

Chapter 11. Smart Cities and Connected Vehicles

11.1 Intelligent Transportation and V2X Communication

11.2 Urban Governance Scenarios

11.3 Ultra-Large-Capacity Architecture Challenges

11.4 AI Traffic Prediction and Optimization

11.5 Engineering Practice and Case Study

Chapter 12. Agricultural IoT and Environmental Monitoring

12.1 Precision Agriculture and Environmental Sensor Networks

12.2 AI in Agriculture

12.3 Low-Power Wide-Area Network (LPWAN) Technology Selection

12.4 Engineering Practice and Case Studies

Chapter 13. Trusted Data Collaboration, Decentralized Identity, and Privacy-Preserving Computation

13.1 Overview of Blockchain and IoT Convergence

13.2 Device Identity and Trusted Data

13.3 Supply-Chain Traceability

13.4 Decentralized IoT Architecture

13.5 The AI + Blockchain + IoT Triangle Paradigm

13.6 Implementation Essentials and Frontier Directions

Chapter 14. IoT DC3 in Practice: From Platform to Agent Applications

14.1 Overview of the Full Project Lifecycle

14.2 An End-to-End IoT DC3 Project

14.3 Common Pitfalls and Best Practices

14.4 Outlook and Summary

Appendix

Copyright & License

On this page

CHAPTER 07

07

Chapter 7

AIoT and Agent Applications

From Spring AI, RAG, tools, and MCP into an industrial Agent Runtime — how probabilistic decisions are constrained by state, permissions, workflows, recovery, and audit into bounded autonomy.

FROM INDUSTRIAL SOFTWARE TO AI AGENTS

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Previous section6.4 Engineering Summary and Further Reading

Next section7.1 The AIoT Technology Landscape and Evolution

From Industrial Software to AI Agents · Building a multi-protocol, cloud-native, open-source industrial IoT platform ready to evolve toward AI agents

By Zhang Hongyuan · © 2016–2026