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.
Meta, Software Engineer
Hugh Nguyen is a Software Engineer working on Metamate team at Meta, which builds AI powered products and platforms. Hugh is working on enabling AI agents to access all internal knowledge through GraphQL, a novel approach to rapidly expand AI agent's capabilities.
Meta, Engineering Manager
Engineering Manager for integrating GraphQL in our mobile applications at Meta. Previously focused on React at Meta.
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