Context and Background
Regular expressions (regex) are powerful for pattern matching but notoriously difficult to write, read, and debug. Even experienced developers often rely on trial-and-error or online tools to craft complex regex patterns. Meanwhile, non-technical users are effectively locked out of using regex in their workflows.
Small Language Models (SLMs) like Gemma can bridge this gap by understanding natural language intent and generating accurate, optimized regex patterns. This session introduces PatternPilot, an AI-powered agent that converts everyday language into regular expressions — making pattern matching more intuitive and accessible across technical skill levels.

Session Abstract
This session demonstrates how to build and deploy PatternPilot, a production-ready ADK agent on Cloud Run that acts as a natural language-to-regex translator. By leveraging SLMs for structured outputs and instruction-following, PatternPilot simplifies the process of creating and debugging regex. Attendees will learn how to operationalize PatternPilot—from model integration and Cloud Run deployment to validation workflows and autoscaling under load. Whether you’re a developer, data scientist, or business analyst, this session shows how to make pattern matching accessible to everyone through conversational AI.

What the Session Covers
• Introduction to SLMs and why they excel at structured tasks like text generation
• Overview of ADK and Cloud Run for agent deployment
• Implementing PatternPilot to:
o Convert natural language into valid regex expressions
o Translate regex patterns back into human-readable explanations
• Deploy and scale PatternPilot on Cloud Run
• Validating interactions through the ADK interface
• Observing autoscaling and performance under load

Key Takeaways
• Learn to operationalize a bidirectional regex translator using ADK
• Understand how SLMs make complex pattern generation more intuitive
• Gain practical insight into Cloud Run deployment and autoscaling for AI agents
• Discover how conversational regex tools can accelerate developer productivity and reduce syntax errors