Built to Evolve: 13 Years of GraphQL
summary
Meta explains why GraphQL needs new ecosystem primitives: the canonical schema is massive (49 MB SDL = 12–15 million tokens while largest AI context is ~1 million), agents now generate queries and change code fast (agents add fields hundreds of times a day), and AI-assisted tooling already cuts operational load (Midway reports a 60% drop in GraphQL support). Concrete work focuses on discoverability, context-slicing (retrieval/chunking/summarization), and tooling to remove dead fields so agents can read and act on the schema reliably.
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chapters (7)
- 0:00Introduction and context
- 3:09The history of GraphQL
- 7:36GraphQL in the era of AI
- 11:17Core challenges for agents
- 15:38Unlocking schema value
- 17:00The next chapter
- 19:24Closing thoughts
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