Context and Background
Working with databases often requires switching between two worlds: business intent expressed in natural language and precise SQL syntax. Non-SQL users struggle to write queries, while developers and analysts sometimes need to explain SQL back to stakeholders. Small Language Models (SLMs) are well-suited for this bidirectional translation, making database access and collaboration more seamless. This session introduces SQLSense, an ADK-powered agent that converts natural language into SQL queries and translates SQL back into plain intent.

Session Abstract
This session demonstrates how to build and deploy SQLSense, a production-ready ADK agent on Cloud Run that acts as a bidirectional SQL translator. By leveraging SLMs for structured outputs and instruction-following, SQLSense enables developers, analysts, and non-technical users to bridge the gap between human language and database syntax. Attendees will walk through implementation, deployment, validation, and scaling patterns for this agent.

What the Session Covers
• Introduction to SLMs and why they excel at structured tasks like query translation
• Overview of ADK and Cloud Run for agent deployment
• Implementing SQLSense to:
o Convert natural language intent into SQL queries
o Translate SQL back into human-readable explanations
• Deploying SQLSense to Cloud Run with environment configuration
• Validating interactions through the ADK interface
• Observing autoscaling and performance under load

Key Takeaways
• Learn how to operationalize a bidirectional SQL translator with ADK
• See how SLMs can simplify query generation and explanation
• Understand Cloud Run deployment workflows and autoscaling behavior
• Gain practical insight into building AI agents for data-driven teams