From Query to Conversation: GraphQL as an AI Interface Layer

Session description

How do you teach AI to navigate GraphQL schemas with thousands of fields? At Meta, we built an AI system that dynamically discovers and loads subschemas on-demand, enabling natural language interactions with complex enterprise APIs. This talk shares hard-won lessons from building production AI that performs real-time schema exploration, manages dynamic subschema composition, and generates sophisticated GraphQL operations at Meta's scale. Key Topics: - Dynamic schema discovery from user intent - On-demand subschema loading architecture (@require_graphql_subschemas directive) - Teaching LLMs GraphQL type relationships and dependencies - Performance optimizations for real-time schema introspection - What failed and why certain approaches don't scale Lessons from Production: - Schema design principles that work better with AI Security considerations for AI-driven schema access - Operational challenges and monitoring strategies - Attendees leave with battle-tested patterns for conversational GraphQL systems, specific techniques for dynamic schema loading, and honest insights about what didn't work along the way.