We deploy a two‑phase loader: prepare the candidate off‑screen, validate its manifest against policy, run through a qualification loop with rendered samples, commit synchronously if it passes, and restore the last known‑good state on any failure.

AI assists by generating variations and scaffolding, but humans define the boundaries for identity, integrity, device policy, runtime qualification, commit semantics, rollback, and cleanup.

To map to Tencent RTC Beauty AR, use a narrow adapter interface where prepare observes, probe collects metrics, activate is atomic, restore enables rollback, and dispose performs cleanup.

AvatarLoader demonstrates the flow with manifest validation, qualification cycles, and activation/rollback logic, including handling of supersession and cleanup.

Policy framework introduces device tiers (low, mid, high) with thresholds for declared asset size, qualification frames, max bad frames, and max frame time, all defined by the app and the device tier.

Testability is ensured with a fake port and vitest tests that verify control flow—commit after qualification, restore on activation failure, and disallow 3D on disallowed tiers.

Intro: AI-assisted avatar creation is fast but requires a controlled two‑phase loader to guarantee safety and presentation integrity during live sessions.

Release checklist covers all steps before enabling 3D avatar updates: prepared assets, validated manifest, device tier measurement, full qualification coverage, transactional semantics, rollback, no‑avatar state, disposal, and cross‑device testing.

Loader state machine enforces explicit visible transitions—preparing, qualifying, committing, and failing—with accessibility support and detailed logging.

Invariant: a candidate avatar must not go live until it passes policy validation and runtime qualification; if none is committed, show a placeholder or paused state to avoid broken frames.

Loader architecture centers on AvatarPort with prepare, probe, activate, restore, and dispose; activation is synchronous to prevent race conditions and supports transactional presentation semantics.

Failure drills and tests outline corruption, rapid selection changes, tracking loss, device-tier rejection, activation failure after qualification, and cleanup failures, guiding test design and observability.