FrameBright.ai/Generative gaming/Classification loop
Generation-then-classification

Generated, then verified. Always in that order.

Every generated frame, line of dialogue, and reward asset passes AEGIS before display. The loop is closed; the kid never sees the raw generator output.

The loop

  1. The seed engine generates the next beat: scene, dialogue, reward.
  2. Every artifact is classified by AEGIS before it is queued for display.
  3. If AEGIS clears, the artifact ships to the renderer.
  4. If AEGIS holds or flags, the seed engine regenerates; the canonical fallback ships if regeneration fails repeatedly.
  5. Every shipped artifact carries a classification receipt the platform can audit.

Latency budget

Classification must not block rendering. The pipeline budgets classification at sub-frame for short text, at sub-second for image and audio assets, and overlaps generation of beat N+1 with classification of beat N. The kid-perceived latency floor is the renderer, not AEGIS.

Failure modes

  • Single-fail: regenerate. The seed engine retries with a perturbed prompt and a tightened constraint set.
  • Repeat-fail: fall back to a canonical variant from the curated library.
  • Hard-fail: if even the canonical variant cannot be cleared (rare; usually a configuration error), the session ends with a graceful prompt rather than an unsafe display.

Evidence chain

Every generated artifact carries a classification receipt: which AEGIS version classified it, the verdict, the confidence band, and the per-detector evidence trace. The receipts are auditable end-to-end; a parent dashboard, a regulator, or a research partner inspecting a session sees the actual evidence the engine used.

Receipts are designed to feed Veritize for structured-evaluator workflows. See Veritize /for-agents for the structured-evaluator approach this loop integrates with.