How to adopt AI plugin for Redmine Helpdesk
Hi, I'm Marat from RedmineUP Cloud. We're about to ship AI Agent , a Redmine plugin with a built-in MCP server. Before releasing it, we let our own support team use it on real customer tickets. Turns out the hard part isn't the LLM. It's the order you introduce things in . The data is already there If your support runs on Redmine, you're sitting on years of solved tickets, wiki pages and customer…
Marat from RedmineUP Cloud is set to release AI Agent, a Redmine plugin equipped with a built-in MCP server. During the testing phase, it was discovered that the most challenging aspect was not the Language Model (LLM) itself, but rather the sequence in which elements are introduced. The key insight lies in the existing data within Redmine - years of resolved tickets, wiki pages, and customer histories that remain untapped.
This is where RAG (Retrieval Augmented Generation) comes into play, making this knowledge accessible and usable.
The learning process unfolds in five steps:
1. Retrieval of Answers from Existing History: The AI Agent utilizes Redmine's native search to fetch information from wiki pages and issues. The answers generated are directly linked to their source, such as ticket #4521 and the specific wiki page on Single Sign-On (SSO).
2. Triage and Draft Preparation: Upon receiving an email that becomes a ticket, the agent assigns a category, priority, and assignee based on existing IDs in Redmine. It then drafts a response and saves it as a private note, ensuring no direct communication with the customer. This process involves three crucial rules: using closed lists to reject any unauthorized values, refraining from automatic sending, and maintaining private notes to prevent any leakage.
3. Generating Reports: Through the MCP platform, a support lead can request specific reports, such as median and P90 first response times for a particular project, the share of issues answered within four hours, and trends by year. The AI Agent compiles the necessary data, creates charts, and explains the long tail - the less frequently occurring data points.
4. First-Line Agent Functionality: The AI Agent is designed to auto-reply in narrow categories with high accuracy, such as password resets and inquiries about invoices. For other issues, it forwards the ticket to a human agent after providing a concise summary of the AI-generated response.
5. Integrating Customer and Helpdesk Data: The AI Agent consolidates customer and helpdesk data into a single query. It computes scores like Lifetime Value (LTV) in the code itself, allowing the LLM to merely interpret and explain these figures, ensuring reproducibility of the numbers.
In summary, the most effective starting point for integrating an AI agent within a support queue is not to deploy a chatbot, but rather to focus on retrieval, gradually introduce drafts, evaluate the outcomes through rigorous measurement, and automate processes only when the data substantiates its safety and efficacy.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
