codex-scenario-consistency

To keep ChatGPT scenario behavior consistent, you align recall, model routing, plan mode prompts, and request translation by applying minimal patches across all relevant entry points to prevent downstream drift.

Downstream drift in Codex recall and model routing happens when scenario optimizations are applied to one entry point without corresponding patches in intent routers, local search, and request translators.

You align Codex behavior by identifying target layers like intentRouter, localSearch, and translator, then applying the smallest feasible patches that respect existing invariants and reuse established components.

Yes, you can and should reuse existing components when patching Codex plan mode prompts and routing to maintain system stability and respect established invariants across all patched layers.

The best way to validate end-to-end behavior after aligning Codex scenario optimizations is to execute real calls through the system to confirm recall, routing, and prompting consistency across all entry points.

You should patch the request translator when scenario optimizations are introduced in intent routing or plan mode prompts, ensuring the translator applies matching behavior to prevent downstream inconsistency.