Closing the Loop: How GraphQL Gives Coding Agents Eyes on What Actually Matters
summary
GraphQL supplies field-level usage data that coding agents use to stop guessing; the demo builds a clickable events details page with reviews using Codex and DNX/NPX skills, creates a mock against live data, extracts fragments to produce a schema diff via a prototype-to-contract step, and instructs agents to implement microservices one at a time. Prototype steps take ~2 minutes, backend generation ~8 minutes; large schemas (115 MB meta, 12 MB Netflix) force diffs. Validation runs against production via client/schema registries and a local orchestrator Aspire.
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chapters (10)
- 0:00GraphQL and agentic coding
- 3:19Front-end loop with GraphQL
- 8:11Prototyping with GraphQL mocking
- 12:03GraphQL fragments for UI
- 14:36Mocks to GraphQL contracts
- 18:16Large schemas in agent workflows
- 21:10Agent loops and production validation
- 24:40GraphQL trusted documents
- 26:02Agent environments with distributed queries
- 28:07Why GraphQL suits humans and agents
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