## Summary
The planner artifact matches the supplied SHA-256 in its run index and honors the binding choice: open Korean connective prose, immutable locally expanded semantic anchors, deterministic validation, and canonical fallback—not a finite rewrite catalog or a false semantic proof. It also preserves the existing delivery and authority boundaries through default-off/shadow rollout, direct OpenAI-only calls, no retries, durable weekly pins, adaptive atomic persistence, and an ordered legacy cutover; one medium isolation gap needs resolution before implementation.

## Claims
- The source artifact is recorded as `b8616130c4fe03a5d6a2084be6f5a710becb38115a602ed829e1e39bd0b1d3dc` in `plans/ralplan/.../index.jsonl` for planner stage 1.
- The plan explicitly accepts unrestricted connective prose around local placeholders, while preserving every value, decision, action, safety state, approval/delivery state, and layout boundary locally (stage-01-planner.md:5-9, 28-41).
- It explicitly scopes the provider to direct OpenAI, excludes Kimi/Moonshot, generic/auxiliary routes, OAuth, pools, retries, alternate providers, and repairs (stage-01-planner.md:45).
- It keeps the feature presentation-only/default-off and does not activate customers or alter consent, routing, Gate-D, approval, or delivery authority (stage-01-planner.md:9).
- Weekly prepared-delivery replay is explicitly pinned/read-only under the existing schedule lock; adaptive publication is revalidated and atomically persisted under the current authority locks (stage-01-planner.md:22, 68-70).

## Analysis
### Spec compliance
The plan respects the user’s decision rather than replacing it with templates: model prose is open-ended, but raw facts and all truth-bearing claims remain local placeholders. Its validator requires exact schema/echo/order, NFC, bounded output, no unissued identifiers, and lexical/structural rejection of new facts, measurements, safety/medical, action, approval, and delivery assertions (stage-01-planner.md:30-39). It honestly defines residual Korean paraphrase risk as best-effort rather than a theorem, as required.

It honors the OpenAI-only boundary. The existing finalized-feedback path calls the generic `resolve_provider_client` (`gateway/platforms/telegram.py:6148-6189`); the plan instead removes that finalized-render route and gives the postprocessor a dedicated module which imports no generic/auxiliary resolver (stage-01-planner.md:20, 26, 45). Current profile configs have empty `fallback_providers`, but this new direct boundary correctly avoids relying on profile routing.

### Operational safety
The plan is aligned with the canonical operational contract: no delivery authority mutation; no model call for untyped, recovery, terminal, or malformed surfaces; a one-call/no-retry journal; redacted metadata-only retention; and shadow mode that discards selected text before any Telegram, schedule, adaptive payload, log, or telemetry boundary (stage-01-planner.md:9, 41, 45-51). This is stronger than the current final feedback route, which swallows model errors but uses the generic resolver.

For scheduled weekly delivery, the proposed `load_prepared_customer_task_delivery` repairs the present rerender-on-replay risk: current `reserve_customer_task_delivery` rejects a duplicate reservation when immutable body/destination/registry/config pins differ (`customer_schedule.py:1348-1428`), and the plan instead requires the exact existing durable body/destination with a hard stop on any ledger/fence/pin failure (stage-01-planner.md:22). The same helper is explicitly required in both profile copies.

For adaptive cards, replacing the adapter’s current `mark_publish_pending` call (`gateway/platforms/telegram.py:7329-7333`) with a method that reloads pins under `_authority_session_lock()` and `_publication_ledger_lock()` is the correct source-level boundary. The current public method validates payload/provenance but does not own a presentation binding; the proposed binding digest plus revalidation before append closes that TOCTOU window (stage-01-planner.md:68-70).

The migration is concrete rather than a compatibility shim: it audits both actual configs before parser removal, requires an enabled active physique bridge, rejects the legacy key after cutover, preserves evidence, and rolls back only by disabling the new feature—not by restoring the obsolete generic-feedback path (stage-01-planner.md:74-84). Verification covers malformed response attacks, privacy, no retries, locks/journals, schedule recovery, adaptive races, explicit marker collection, and four-surface coordinator-driven shadow proof (stage-01-planner.md:101-119).

### Antithesis and synthesis
No finite lexical/stem detector can establish that arbitrary Korean prose conveys no novel factual or normative implication. The plan acknowledges that exact limitation (stage-01-planner.md:36, 39, 123) and compensates with canonical semantic anchors, strict structural gates, shadow evidence, surface-by-surface promotion, and immediate canonical selection on postprocessor failure. That is the strongest viable design under the user’s explicit acceptance of best-effort semantics; changing to a finite phrase catalog would contradict the chosen open-prose requirement.

The remaining gap is not semantic theory: the selected SDK construction has ambient environment inputs outside the plan’s stated direct-provider seal.

## Root Cause
`openai.OpenAI` is not API-key-only when optional constructor values are omitted. In the pinned `openai==2.24.0`, its constructor reads `OPENAI_ORG_ID`, `OPENAI_PROJECT_ID`, and `OPENAI_WEBHOOK_SECRET` (`.venv/lib/python3.12/site-packages/openai/_client.py:130-160`); organization/project are emitted as request headers (`:340-347`). The planned invocation omits those arguments while asserting that only `OPENAI_API_KEY` is read.

## Findings
- **MEDIUM — reported as P2: Reject implicit OpenAI SDK environment overrides.** `stage-01-planner.md:45` promises configuration fail-closed and API-key-only direct construction, but the specified OpenAI SDK call consults additional ambient variables that can alter organization/project routing. Add a fail-closed preflight for `OPENAI_ORG_ID`, `OPENAI_PROJECT_ID`, and `OPENAI_WEBHOOK_SECRET` (with a regression test), or replace it with a demonstrably isolated client construction. Keep the endpoint and literal model fixed.

## Recommendations
1. Before implementation, amend the direct-client contract and tests so ambient OpenAI organization/project/webhook environment variables make the feature unavailable with canonical/no-call; assert no request/client is constructed in that case.
2. Keep the existing plan’s direct SDK path—do not reintroduce `resolve_provider_client`, profile fallbacks, Kimi/Moonshot, retries, a provider pool, or a semantic compatibility route.
3. Keep promotion gated on the documented 48-call shadow proof and per-surface shadow evidence; do not treat shadow success as Gate-D, customer activation, delivery approval, or a semantic guarantee.

## Architectural Status
WATCH

## Code Review Recommendation
COMMENT

## Tradeoffs
- **Direct SDK with ambient defaults:** lowest implementation effort, but violates the declared sealed OpenAI configuration and permits unnoticed organization/project routing changes.
- **Reject ambient SDK override variables (recommended):** preserves the standard SDK and fixed endpoint/model while making the direct-provider boundary fail closed.
- **Custom isolated HTTP client:** can avoid SDK ambient defaults but adds transport/auth/error-handling maintenance and is unnecessary if preflight isolation is tested.