Agentic Workflow Ticketing: Practice of Enterprise Intelligent Ticketing System Based on Multi-Agent Architecture

This article introduces the open-source project Agentic Workflow Ticketing, which integrates multi-agent collaboration, RAG knowledge base, MCP tool interface, and Feishu IM. It enables end-to-end automation from natural language input to automatic ticket assignment, processing, and auditing, aiming to address pain points such as low efficiency and difficult collaboration in traditional ticketing systems.

Traditional enterprise ticketing systems have problems like cumbersome forms, inefficient cross-departmental collaboration, opaque processes, and difficulty in knowledge accumulation. With the maturity of large language model technology, the Agentic Workflow architecture has emerged, endowing AI with the ability to proactively execute tasks, coordinate resources, and follow up on processes, becoming a new solution.

The system adopts a multi-agent collaboration architecture, including agents for intent recognition, assignment decision-making, knowledge retrieval, process promotion, audit recording, etc. It deeply integrates RAG technology, supporting diverse knowledge sources, dynamic retrieval, and continuous learning. Through the MCP protocol, it achieves tool ecosystem decoupling, security control, and scalability. It also integrates with Feishu IM to provide natural language entry, intelligent notifications, rich interactions, and group collaboration capabilities.

The system workflow is divided into three stages: 1. Intelligent Acceptance: Parse user natural language, extract key information, retrieve knowledge, assign tickets, and generate structured records; 2. Collaborative Processing: Push notifications, support operations and reassignment within Feishu, and automatically associate communication records; 3. Closed-Loop and Auditing: Trigger satisfaction surveys after confirmation of resolution, archive lifecycle data, update the knowledge base, and generate audit reports.

The system can be applied in scenarios such as IT Service Management (ITSM), human resources services, customer service support, and internal approval processes. It helps enterprises lower operational thresholds, improve processing efficiency, ensure response consistency, and optimize employee and customer experiences.

Technical highlights include asynchronous task scheduling (ensuring instant response), context management (isolation and state transfer), human-machine collaboration boundaries (combination of AI automation and manual decision-making), and fault tolerance and degradation (ensuring business continuity). Deployment requires environment requirements such as Python 3.10+ and PostgreSQL 14+. It can be quickly launched through steps like cloning the repository, installing dependencies, configuring environment variables, and completing Feishu application configuration.

Current limitations include the need to improve understanding of Chinese professional terms, limited multi-modal support, and insufficient automation of complex processes. Future plans include adapting to multiple platforms (DingTalk, WeChat Work), integrating voice interaction, predictive maintenance, and providing a visual process orchestration interface.

Agentic Workflow Ticketing demonstrates the practical value of Agentic AI in enterprise scenarios, providing a reference for the intelligent transformation of traditional ticketing systems. With the improvement of large model capabilities and the deepening of enterprise digital transformation, such solutions will be applied in more business scenarios and become important tools to improve operational efficiency and employee experience.