The bridge that selects what feeds the validator — audited by the validator
Fourteen iterations of architect.validate against the orchestration code that bundles applicant repositories for review. The validator caught real gaps in its own delivery pipeline at every pass. The cert reviewer confirmed the production_ready verdict on iter14. Each readiness-review URL below is public — every score, every verdict, every recommendation is replayable.
Key Facts
- Validator iterations
- 14 prod-MCP runs
- Score trajectory
- 35/F → 100/A
- Cert outcome
- confirmed_production_ready
- Missed defects
- 0 (cert second-pass)
The claim, stated narrowly
AI Design Blueprint's `architect.validate` tool grades agentic code against the 10-principle doctrine. The cohort-bridge orchestrator is the code that converts an applicant's public GitHub repository into validate-compatible input. That orchestrator was submitted to the validator it serves, fourteen times in one evening — first-pass validate plus second-pass cert review on the final iteration.
Layer 1: validator scores its own source (cert-confirmed self-audit, 13 prior rounds). Layer 2: validator scores a governed agent (canary baseline 100/A clean, 18/F injected). Layer 3: validator scores the bridge that selects what to feed it — this case study. Three layers, each auditing the layer below, each with public receipts.
Cert-confirmed receipt
Iter14 second-pass cert summary, verbatim from the persisted review:
CERT CONFIRMEDThe first-pass production_ready verdict is confirmed: the code shows durable job states, explicit terminal/blocked/aborted handling, retry paths, and no specific missed defect that would currently cause a real user silent wrong results, crash, or trust-boundary bypass.
Trajectory across fourteen iterations
Click any iteration to open its public readiness review. The validator's verdicts, per-principle reasoning, and severity scores are rendered live from the persisted run.
What the trajectory reveals
Score-as-snapshot misses the iteration story. The bridge hit production_ready three times (iter8, iter9, iter10) before iter11 regressed to emerging on identical code — a single LLM-variance event that promoted P10 from hardening_recommended to production_blocker. The fix path then required four more iterations: durable steering primitives (iter12), step-aware recovery + terminal-status guards (iter13), and the post-validate abort re-check that closed P10 definitively (iter14).
The cert reviewer's iter10 downgrade caught a real bug — but in the submission discipline, not the underlying code. The excerpt sent to validate was missing `from app.db.session import Base`. The actual repo file always had it. The cert reviewer's verdict prose said so explicitly. Iter11 restored the import; the regression that immediately followed was the variance event, not a code change.
The doctrine the validator applies is the same doctrine that runs inside Claude Code via MCP. Every recommendation in every iteration is mechanism-specific (named identifiers, named files, named race conditions). None are template-generic. The iter14 cert summary names which terminal states and retry paths confirm the verdict.
Verbatim from iter2 — the P7 finding the iter1 review hadn't reached
DRAFTThe audit envelope is a substantial improvement: `audit_object` records `schema_version`, `captured_at`, source `repo_url`/`commit_sha`, selected file hashes, skipped reads, `bundle_sha256`, validator latency, and shape-only log signals. However, the bridge still constructs the validator payload by concatenating raw untrusted repo content in `_build_implementation_context()` via `chunks.append(f"# === FILE: {path} ===\n{content}\n")` and then sends it as `ValidationRequest(code=bundle, ...)`. This file does not establish or record an inert untrusted-input boundary, so repo comments or filenames containing instruction-like text could steer the validator unless a hidden downstream service compensates.
Verbatim from iter14 — P10 steering, finally aligned
CERT CONFIRMEDSteering primitives are now present for both validation and onboarding recovery. `request_abort()` persists `abort_requested=True`; `_execute_with_job()` checks for abort before validation and, in the Iter14 change, refreshes the job and re-checks `job.abort_requested` immediately after `asyncio.run(validate_code_against_principles(...))` returns and before creating `UserValidationRun` or calling `mark_completed()`, discarding validator output on abort. `retry_failed_validation_job()` requires the latest job to be terminal and `retry_eligible`, while `retry_onboarding_handoff()` preserves the failed state (`firebase_user_failed`, `sign_in_link_failed`, or `approval_email_failed`) and clears only `onboarding_failure_reason`, allowing `approve()` to re-enter step-aware recovery.
What the bridge is now
An end-to-end orchestration surface that converts an arbitrary public GitHub repository into a validator-compatible bundle, with durable workflow state, typed file-envelope boundaries, step-aware operator recovery, post-validate abort honoring, and a cert-confirmed production_ready badge that any reader can verify by re-fetching the run_id. The first 10 cohort applicants this week will be onboarded through this code.
Three case studies, three facets
This is the self-validation facet of the recursive-integrity triad. The other two facets are public: the substrate-validation scan on Anthropic's own agent-SDK demo code, and the reference-honesty scan on our own A2A protocol example. Same doctrine, three code surfaces, three different verdicts.
Receipts
Every claim above is replayable from the public run URLs. The 10-principle doctrine fingerprint stayed constant across all fourteen iterations (validator confirmed `same_doctrine` on every baseline comparison). No mid-stream rule changes; the same rubric scored every run.
Re-validation result
After Iter14: architect.certify confirmed production_ready
The iter14 implementation was validated and then certified in the same prod-MCP session. Cert outcome: confirmed_production_ready. The badge is live and the readiness review is publicly inspectable.
Calculated ROI
Same metrics, same calculator powering every case study
Derived deterministically from this case study's profile (14 iterations, code-modifying blast radius, autonomous workflow, under compliance) via /lib/case-study-roi.ts. Numbers directly comparable to the other case studies.

