How GraphQL Gives Coding Agents Eyes on What Actually Matters
summary
Builds a clickable UI prototype, auto-extracts GraphQL fragments, and generates a schema diff plus a backend plan that the agent implements; the demo creates an events detail page with a comments section, produces mutations and payload/error types, and implements Hot Chocolate resolvers, mediator-pattern commands, and cursor pagination. Early steps take ~2–3 minutes, backend planning and implementation tooling take ~20 minutes, and customers report precise, reviewable outputs. Key pieces: prototype→fragment→schema and schema+client registries enabling field-level usage-aware agents.
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chapters (9)
- 0:00Introduction to AI coding
- 1:46Why GraphQL fits AI workflows
- 3:12Starting with UI prototypes
- 7:55Using AI prototype skills
- 12:50From fragments to schema changes
- 16:22Generating backend implementation
- 23:56AI precision in business code
- 24:59Schema registries for AI feedback
- 26:26Closing and Q&A
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