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 presents three typical, progressively advanced practical cases. These cases cover the complete path from a basic Q&A assistant, to a complex business process assistant, and finally to rapid reuse of existing workflows. By studying these three cases, users will fully master how to choose the appropriate building method based on actual business needs, enhancing their capabilities in digital intelligent work.
🧩 Case 1: Building a Simple Knowledge-Based Q&A Agent from Scratch
This case is designed for beginners and focuses on how to create a knowledge-based intelligent Q&A Agent from scratch. This Agent can automatically retrieve information from the enterprise knowledge base and intelligently respond to user queries, thereby improving the efficiency of knowledge acquisition.
In practical scenarios, such as M365 operations engineers who often need to consult a large number of technical documents, operation guides, and troubleshooting cases during daily operations, the diversity and scattered nature of document resources make manual searching inefficient and prone to repetitive work.
With the guidance of this tutorial, users will learn how to quickly build a usable intelligent Q&A assistant on the AI Centra platform and accomplish:
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Knowledge base integration and management
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Agent capability configuration and debugging
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Optimization of question recognition and answer matching mechanisms
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 via Workflow
When business requirements go beyond simple Q&A, such as the need to operate across multiple systems and handle multi-step business logic, Agents created using basic methods are no longer sufficient. At this point, you can leverage the Workflow mechanism provided by AI Centra to achieve more advanced intelligent agent capabilities.
This case will guide users through a real business process example to build an intelligent Agent with abilities such as "judgment, decision-making, external service invocation, and loop execution," covering the following key capabilities:
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Workflow node configuration and connection methods
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Conditional judgment and data processing
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Multi-system integration and task chain management
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Failure handling and exception branch design
This type of Agent is suitable for scenarios such as complex form approval, automated data processing, and cross-system task orchestration, making it an important tool in enterprise intelligent transformation.
⚡ Case 3: Quickly Creating Workflow Agents Using Configuration Codes
When there is a need to rapidly reuse existing mature workflows, the configuration code sharing mechanism can be used for quick Agent deployment. This case uses the "Sensitive Word Extraction" assistant as an example to demonstrate how to quickly share complex workflow capabilities within a team using configuration codes.
The feature of quickly creating workflow Agents via configuration codes provides users with an efficient and convenient Agent reuse solution, greatly improving workflow deployment efficiency and consistency, with the following advantages:
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Efficient reuse, saving development costs, and one-click copying of mature Agent templates
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Maintaining workflow processing standards consistently within teams or organizations
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The same core workflow can be adapted to different business scenarios through fine-tuning
This Agent creation method is suitable for cross-team collaboration and multi-scenario business expansion that require rapid and standardized replication and deployment of mature workflows.
Through these three practical tutorials, users will gradually build a systematic understanding of intelligent Agent construction methods and be able to flexibly choose technical paths according to their own business scenarios, achieving an intelligent leap from automated Q&A to automated execution.