GraphQLConf 2026

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.