Context and Background
In real-world software projects, developers and stakeholders often create JIRA tickets with only minimal input—such as a vague summary or a one-line description. Missing fields like acceptance criteria, priority, or labels can cause long-term issues: poor backlog grooming, unclear requirements, and slower resolution cycles. Writing high-quality tickets requires context, structure, and consistency, which can be time-consuming for developers and product managers. Small Language Models (SLMs) excel at structured expansion and can fill these gaps consistently. This session introduces TicketTuner, an ADK-powered agent that transforms rough inputs into complete JIRA tickets, automatically generating fields such as description, acceptance criteria, priority, and labels.

Session Abstract
This session demonstrates how to build and deploy TicketTuner, a production-ready ADK agent on Cloud Run that turns raw, incomplete inputs into structured JIRA tickets. By leveraging SLMs for instruction-following and text structuring, TicketTuner ensures that key fields—description, acceptance criteria, priority, and labels—are consistently populated. Attendees will learn how to implement, deploy, and validate the agent, and see how Cloud Run enables scalable, production-ready operation.

What the Session Covers
• Why incomplete tickets hinder project health and velocity
• Overview of SLMs for structured task expansion
• Implementing TicketTuner to populate:
o Description
o Acceptance criteria
o Priority
o Labels
• Deploying the agent on Cloud Run with ADK
• Validating outputs through the ADK interface
• Analyzing autoscaling and performance under load

Key Takeaways
• Learn how to operationalize an ADK agent that improves JIRA ticket quality
• Understand how SLMs can enforce consistency in project management workflows
• Gain hands-on insight into deploying agents with ADK and Cloud Run
• See how structured ticketing supports long-term project clarity and velocity