Shadow Traffic Mirroring — OpsDeploy

Safely mirror real production requests to a new model or service without affecting users. Use this page to validate output drift, latency, rate limits, and side-effect isolation before any canary or switchover.

Open these first

Visual map and recovery: RAG Architecture & Recovery
End to end retrieval knobs: Retrieval Playbook
Live ops: Live Monitoring for RAG, Debug Playbook
Safe rollouts nearby: Staged Canary, Blue-Green Switchovers
Backpressure and retries: Rate Limit Backpressure, Retry Backoff

Acceptance targets

ΔS(prod_answer, shadow_answer) ≤ 0.45 on three paraphrases
λ remains convergent across two seeds
P99 added latency from mirroring ≤ 5 percent of end-to-end
Zero side effects from shadow path: writes blocked or redirected
Sampling accuracy within ±2 percent of configured shadow ratio

60-second checklist

Mirror only reads
Route the same request payload to the shadow service. Strip tokens and secrets not required for read paths. Block tool calls and any writes.
Tag and store
Append shadow_id, req_hash, model_rev, index_hash. Persist both prod and shadow outputs with ΔS and λ.
Throttles
Apply a hard cap on mirror QPS. Respect provider limits. Enable backpressure guards.
Drift gates
Alert when mean ΔS exceeds 0.45 or when λ flips on harmless paraphrases.

Minimal playbook

Ingress: duplicate the request at the edge or gateway. Never await the shadow response on the user path.
Sanitize: remove side-effect headers, redact PII fields that the shadow does not need.
Observe: log ΔS, λ_state, shadow_latency_ms, HTTP codes, rate-limit headers.
Compare: evaluate citation alignment with Retrieval Traceability and snippet schema from Data Contracts.
Decide: graduate to canary if drift stays within target for 24 hours and error budget is untouched.

Common pitfalls → fix

Shadow answers write or call tools
→ run in read-only mode and stub tool responses. See Idempotency & Dedupe.
Cache pollution from shadow
→ segregate caches by shadow=true key segment. See Cache Warmup & Invalidation.
Latency spikes
→ apply sample ratio caps and separate thread pools. Add Backpressure.
Unreliable comparisons
→ normalize prompts and headers, align indexes with Vector Index Build & Swap.

Escalate

Promote to Staged Canary when drift and error rates meet targets for a full diurnal cycle and shadow P95 latency increase is under 3 percent.

🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

👑 Early Stargazers: See the Hall of Fame —
⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.

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