Overview
Jira queries often require users to remember complex JQL syntax or navigate across multiple Jira dashboards. This slows down agile workflows, delays stand-up meetings, and adds friction for non-technical users. To address this challenge, Optimus Information built Jira Query, an AI-powered Microsoft Teams agent that enables users to query Jira Cloud using natural language right from within Teams.
Jira Query uses Azure OpenAI to translate conversational inputs into valid JQL, fetch results from Jira Cloud (read-only), and present them in an easy-to-digest format within Microsoft Teams. This improves team visibility, reduces time spent on manual lookups, and simplifies Jira usage for broader teams.
The Challenge
Teams relying on Jira for Agile project management face key pain points when extracting data:
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Complexity of writing and remembering JQL queries
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Time lost switching between Jira dashboards, reports, and Teams
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Limited access for non-technical stakeholders to retrieve relevant data
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Inconsistent updates during stand-ups and sprint planning
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Difficulty correlating work items to blockers or bugs in real time
Organizations needed a secure, Teams-native solution to access Jira insights instantly and intuitively.
The Solution
Optimus developed Jira Query, an AI-powered Teams agent that uses Azure OpenAI to convert natural language into JQL and securely retrieve Jira Cloud data (read-only) within the Teams interface.
The agent authenticates with Jira Cloud via OAuth and executes queries without exposing sensitive write operations. Whether users want to view sprint status, list unresolved bugs, or find high-priority tickets, Jira Query delivers actionable answers instantly in Teams.
Key Capabilities
Teams-Native Access
Query Jira directly in Teams chats or channels without opening Jira dashboards.
Secure Jira Cloud Authentication
OAuth-based login to connect with Jira Cloud APIs using read-only permissions.
Natural Language to JQL
No syntax learning needed—users ask questions in plain English, and the agent handles the translation.
Filtered and Contextual Data
Retrieve only relevant issues based on project, status, assignee, or sprint context.
Formatted Responses
Results are displayed as structured cards in Teams with issue summaries, links, and key metadata.
Multi-Agent Architecture
Jira Query is built using a modular multi-agent architecture coordinated by a central orchestrator for scalability and secure data access.
| AGENT | ROLE |
|---|---|
| Orchestrator Agent | Manages user prompts, routing, and query context |
| Auth and Access Agent | Handles secure Jira OAuth login and project scoping |
| Query Translation Agent | Converts natural language to JQL using Azure OpenAI |
| Jira API Agent | Fetches data from Jira and structures it |
| Response Generator Agent | Formats and delivers Teams-ready results |
Deployment
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Platform: Microsoft Teams, Copilot Studio, Jira Cloud
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Auth and Data: OAuth2 (Jira Cloud), Microsoft SSO, Azure OpenAI
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Tech Stack: Azure Functions, Azure SQL, Teams Bot SDK
This ensures secure authentication, reliable API communication, and deployment flexibility across environments.
The Results
By embedding Jira Query into Microsoft Teams, Optimus helped teams cut Jira lookup time, simplify status reporting, and increase participation across technical and non-technical roles.
Business Outcomes
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Up to 80% faster retrieval of Jira insights during stand-ups and retros
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Simplified Jira usage across teams with no JQL learning curve
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Reduced meeting prep time with real-time ticket summaries
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Improved collaboration with instant access to bug or issue data in Teams
Technologies Used
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Azure OpenAI (GPT-4)
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Microsoft Teams
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Microsoft Copilot Studio
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Jira Cloud API
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Azure Functions
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Azure SQL Database
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OAuth2 (Jira Cloud, Microsoft SSO)
What’s Next
The upcoming version of Jira Query may support dynamic filters, sprint comparison views, and advanced summary cards for reporting. Future enhancements may include integration with Confluence and Power BI to automatically generate sprint health reports and dashboards.
