How to Build a Local LLM Agent to Automate Work List Generation from Monthly Reports (With Jira Integration)
The article discusses the development of a local LLM-powered agent designed to automate the extraction of work items from monthly developer reports. This solution addresses issues of data quality, duplicate entries, and security risks associated with cloud-based AI tools. By running entirely on internal servers, the agent ensures data privacy while efficiently processing unstructured report data.
- ▪The management team faced challenges with manual work list generation, which was slow and error-prone.
- ▪The local LLM agent normalizes chaotic report data and generates a clean list of accomplishments.
- ▪The solution runs on a CPU-only server, ensuring data privacy for enterprise clients.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 2061486) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Sergey Laptick Posted on May 21 • Originally published at xbsoftware.com How to Build a Local LLM Agent to Automate Work List Generation from Monthly Reports (With Jira Integration) #ai #llm Our management team spent hours manually extracting work items (“bug fix”, “released version 1”, etc.) from dozens of developer reports. The task was repetitive, error‑prone, and a security risk when using cloud‑based AI tools, since it means exposing internal activity to external servers.
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