Deploy and Traffic Shaping — Guardrails

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Cloud_Serverless — scalable functions and event-driven pipelines

WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Control how new code reaches users so you avoid cold spikes, duplicate effects, and version-skew crashes. This page gives a fast, provider-agnostic playbook for serverless rollouts with RAG, streaming, queues, and webhooks in the loop.

When to use this page

“Zero-downtime” deploys still produce 5xx or p95 jumps.
Webhooks or jobs fire twice during slot swaps or revision flips.
Streaming responses cut mid-flight while traffic shifts.
Vector writes or indexes receive mixed schema during rollout.
Canary looks healthy, then collapses at 25–50 percent.

Open these first

Boot order safety: Bootstrap Ordering
Rollout deadlocks: Deployment Deadlock
First call failure after deploy: Pre-Deploy Collapse
Live probes and rollback: Live Monitoring for RAG · Debug Playbook
RAG wide view for downstream calls: RAG Architecture & Recovery
Contract the payloads you ship: Data Contracts

Acceptance targets

No increase in 5xx rate at each traffic step.
p95 latency delta between old and new ≤ 10 percent at equal load.
Cold-start share ≤ 5 percent after warm-up gates.
No duplicate side effects. Dedupe hit rate ≥ 99.9 percent for idempotent POST.
For RAG: ΔS(question, retrieved) stays within ±0.03 between old and new. λ remains convergent on two seeds.

Fix in 60 seconds

Canary with gates Route 1 → 5 → 25 → 50 → 100 percent only if gates pass: 5xx flat, p95 flat, ΔS stable, queue age flat.
Pre-warm and pin Set min instances or provisioned concurrency for the new revision. Pin a canary header X-Release: new for synthetic traffic before shifting users.
Graceful draining Enable connection draining. Keep old revision serving for N seconds while you stop sending new requests. Do not kill active streams.
Idempotency fence Add Idempotency-Key on all write paths and background jobs. Store in KV for at least release_window + 24 h.
Schema locks Embed INDEX_HASH, SCHEMA_REV, and MODEL_TAG in every request. Reject if the backend is behind. Route user to the matching revision.

Typical breakpoints → exact fix

Version skew between retriever and writer New code writes updated fields but retriever uses old schema. Lock with SCHEMA_REV in requests and refuse cross-rev traffic. Open: Data Contracts
Webhook storms after deploy Retries plus slot swap triggers double delivery. Use dedupe key = sha256(source_id + event_rev + index_hash). Open: Bootstrap Ordering
Canary fine, 25 percent fails New revision saturates cold CPU or NAT. Raise min instances or provisioned concurrency before the 25 percent step. Open: Pre-Deploy Collapse
Streaming cuts during shift Traffic router closes long-lived connections. Enable draining and pin streams to a revision using a sticky cookie or header.
Queues drain twice Two workers on different revisions read the same shard without a fence. Lock by queue_partition + release_id.

Minimal recipes you can copy

A) Weighted rollout with pinned canary

Headers:
- X-Release: canary
- X-Canary: true
Routing:
- 1% → 5% → 25% → 50% → 100%
Gates per step:
- 5xx delta ≤ 0.05%, p95 delta ≤ 10%
- ΔS drift ≤ 0.03 on canary questions (k=10)
- Queue age slope ≤ 0
Rollback:
- instant shift to previous revision if any gate fails

B) Serverless config checklist

Warm-up
- min_instances or provisioned_concurrency ≥ expected p50 load
- warm path hits cache, model, secrets, vector client

Draining
- connection_drain_seconds: 60–120
- keep old revision for draining window after last route

Idempotency
- Idempotency-Key on POST, stored in KV ≥ 24h
- retry policy: 3 tries, jitter, total time < request timeout

Schema pins
- SCHEMA_REV, INDEX_HASH, MODEL_TAG in every call

C) Background jobs and batch

During 1–25% steps:
- pause heavy batch, only allow user-facing flows
- cap queue concurrency per partition
- fence by (partition, release_id)

Observability you must add

Split all metrics by release_id and revision.
5xx, p95, cold-start rate per revision.
Queue age, retries, dedupe_hits.
ΔS and λ on a fixed probe set, old vs new.
Rollout timeline with traffic percent and gate decisions.

Verification

Probe set shows ΔS and λ unchanged within targets.
No duplicate side effects in logs for the rollout window.
p95 and error rate flat across each step.
Draining proves streams finish on old revision.

When to escalate

Any gate fails twice at the same step → freeze traffic, roll back, keep warm, investigate NAT, DNS, or cold-start headroom.
Schema incompatibility detected → hold rollout, bump SCHEMA_REV, re-index or add translation layer, re-run canary.
Sticky routing not respected → switch to cookie pinning or per-request header pin.

🔗 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

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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