Old Jira tickets can slow teams down. This automation hunts for unresolved issues that have been open for seven days or more, then decides the next best step. Support leaders use it to clear backlogs, keep customers updated, and protect response quality.
On a daily schedule, it pulls a list of long lived issues and runs each one in parallel for speed. It gathers every comment, builds a clean thread, and sends the summary to an AI model that classifies the ticket state. When a fix is already reached, it posts a closing note, checks sentiment, and either asks for a review or alerts a Slack channel if the customer is unhappy. If no teammate has replied, an AI agent searches similar Jira cases and Notion pages to draft a clear answer, then comments and closes. The agent uses a structured output parser to keep fields like solution found, short summary, and response clean and easy to audit. If the case is waiting on someone, a reminder is posted, and a guard prevents spam when the last message was from a bot.
Setup needs connections for Jira Software Cloud, OpenAI, Notion, and Slack. Adjust your age filter, JQL, Done status ID, and the Slack channel. Expect strong time savings on ticket triage, fewer stale threads, and faster recovery on negative cases. Start with one project to tune prompts and messages, then roll out across teams.
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