AI can draft the lazy-loading architecture, but a real engineer has to wire it into your Firebase project.

The general pattern — Cloud Functions for filtered queries, client-side pagination, cache invalidation — is structurally repeatable and well-documented. But the specific implementation varies meaningfully based on the actual data schema, access patterns, and existing Flutter codebase, requiring judgment each time.

The performance problem is concrete (8–12s load, 500k+ docs), but success criteria like 'relevant user data' and acceptable cache invalidation behavior are underspecified. An agent can produce a working solution but cannot confirm it meets the team's actual latency targets or data freshness requirements without feedback.

The agent has no access to the Firebase project, existing Firestore/RTDB schema, Flutter codebase, or deployment credentials. It can only generate code based on described requirements, leaving all integration, testing, and deployment to a human.

Incorrect cache invalidation logic or a flawed Cloud Function could serve stale or wrong user data to 500k+ users, or introduce regressions in app startup. Bugs in production Firebase functions affecting live users are costly and potentially hard to roll back quickly.

Architectural tradeoffs — how aggressively to cache, what counts as 'relevant' data per user, how to handle cache invalidation edge cases — require product and engineering judgment. The coding mechanics are automatable, but the design decisions benefit from a senior engineer's review.