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Practical Tutorial

Practical Tutorial Overview: A Step-by-Step Guide to Building Intelligent Agents from Beginner to Advanced​

To help users gain an in-depth understanding of how to build and apply intelligent agents, this tutorial designs three progressive typical practical cases, covering the complete building path from basic Q&A assistants to complex business process assistants, and then to rapid reuse of existing workflows. By studying these three cases, users will comprehensively master how to choose the appropriate building method according to actual business needs, enhancing their digital intelligent work capabilities.

🧩 Case 1: Building a Simple Knowledge Q&A Agent from Scratch​

This case is aimed at beginners and focuses on how to create an intelligent Q&A agent based on a knowledge base from scratch. This agent can automatically retrieve information from the enterprise knowledge base and intelligently respond to user questions, thereby improving knowledge acquisition efficiency.

In specific scenarios, such as M365 operations engineers who often need to consult a large number of technical documents, operation guides, and fault cases during daily operations. Faced with a wide variety of scattered document resources, manual searching is often inefficient and prone to repetitive work.

Through the guidance of this tutorial, users will learn how to quickly build a usable intelligent Q&A assistant with the SERVICEME NEXT platform and complete:

  • Knowledge base integration and management
  • Agent capability configuration and debugging
  • Question recognition and answer matching mechanism optimization

This agent can be widely applied in scenarios such as internal IT support, employee self-service, and training material retrieval within enterprises.

🧠 Case 2: Building Complex Business Process Agents through Workflow​

When business needs go beyond simple Q&A, such as requiring operations across multiple systems and handling multi-step business logic, agents created by basic methods are no longer sufficient. At this time, the Workflow mechanism provided by SERVICEME NEXT can be used to achieve higher-level intelligent agent capabilities.

This case will guide users through a real business process example to build an intelligent agent with capabilities such as “judgment, decision-making, calling external services, and loop execution,” covering the following key abilities:

  • Workflow node configuration and connection methods
  • Conditional judgment and data processing
  • Multi-system integration and task chain management
  • Failure handling and exception branch design

This type of agent is suitable for complex form approvals, automated data processing, cross-system task orchestration, and other scenarios, serving as an important tool in enterprise intelligent transformation.

⚡ Case 3: Quickly Creating Workflow Agents Using Configuration Codes​

When there is a need to quickly reuse existing mature workflows, the configuration code sharing mechanism can be used to achieve rapid deployment of agents. This case takes the "Sensitive Word Extraction" assistant as an example to demonstrate how to quickly share complex workflow capabilities among teams through configuration codes.

The feature of quickly creating workflow agents via configuration codes provides users with an efficient and convenient agent reuse solution, greatly improving the efficiency and consistency of workflow deployment, with the following advantages:

  • Efficient reuse, saving development costs, and one-click copying of mature agent templates
  • Maintaining consistency of workflow processing standards within teams or organizations
  • The same core workflow can be fine-tuned to adapt to different business scenario requirements

The creation method of this agent is suitable for cross-team collaboration and multi-scenario business expansion that require rapid and standardized copying and deployment of mature workflows.

Through these three practical tutorials, users will gradually establish a systematic understanding of intelligent agent building methods and be able to flexibly choose technical paths according to their own business scenarios, achieving an intelligent leap from automatic Q&A to automatic execution.