Feature Flags and Safe Launch — OpsDeploy Guardrails

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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.

Ship new prompts, models, retrievers, tools, or index pointers without surprises. This page shows how to scope flags, gate traffic with measurable targets, and roll forward only when the canary stays green.

Open these first

Readiness gate: rollout_readiness_gate.md
Canary staging: staged_rollout_canary.md
Blue green cutover: blue_green_switchovers.md
Version locking: version_pinning_and_model_lock.md
Cache and invalidation: cache_warmup_invalidation.md
Limits under load: rate_limit_backpressure.md
Ordering and boot fences: bootstrap-ordering.md
Schema contracts and traceability: data-contracts.md, retrieval-traceability.md

When to use

New prompt pack or header order.
LLM provider or model change.
Retriever, chunker, reranker, store, or metric change.
Index pointer or alias swap.
Tool schema or protocol change.

Acceptance targets for a flag to graduate

ΔS drift vs baseline: median ≤ 0.03 and p95 ≤ 0.10 on the gold set.
Coverage to the target section ≥ 0.70.
λ remains convergent across 3 paraphrases and 2 seeds.
p95 latency within +15 percent of baseline. Error rate ≤ 0.5 percent. Tool success ≥ 99 percent.
Cost per solved query within +10 percent or show a clear quality gain.

60-second launch plan

Define the flag and scope
Scope by tenant, cohort, product surface, region, or endpoint. Never rely on client only flags.
Pin versions behind the flag
MODEL_VER, PROMPT_VER, INDEX_HASH, RERANK_CONF, TOK_VER, ANALYZER_CONF. See version_pinning_and_model_lock.md.
Warm the cache for the flagged arm
Use versioned keys and a gold set warmup. See cache_warmup_invalidation.md.
Start at shadow or 5 percent
Compare to a pinned baseline. Hold for one evaluation window.
Graduate stepwise
5 to 25 to 50 to 100 percent if all targets stay green. Else stop and roll back.

Flag taxonomy and use

Kill switch
Immediate global off for a risky module or tool. Server evaluated.
Progressive rollout
Traffic weights per tenant or region with hold times and abort gates.
Quality gated flag
Opens only when ΔS, coverage, and λ are inside bounds for the current window.
Safety guard
Allows cite only output if reason step fails. Protects CX while investigating.
Cost guard
Caps heavy chains. Falls back when cost per solved query exceeds budget.

YAML manifest template

# opsdeploy/flags/feature_flags.yml
flags:
  prompt_pack_vNplus1:
    scope:
      tenants: ["all"]            # or a list of tenant ids
      regions: ["us-east-1"]      # stage per region
      endpoints: ["/rag/reason"]
    versions:
      MODEL_VER: "gpt-4o-2025-07-15"
      PROMPT_VER: "p-2025.08.31-h1"
      INDEX_HASH: "b7f3..."
      RERANK_CONF: "bge-rerank-v2@top32"
    traffic:
      baseline_weight: 0.95
      canary_weight: 0.05
    gates:
      delta_s:
        median_max: 0.03
        p95_max: 0.10
      coverage_min: 0.70
      lambda_convergent: true
      latency_p95_uplift_max: 0.15
      error_rate_max: 0.005
      tool_success_min: 0.99
      cost_uplift_max: 0.10
    abort_rules:
      hard:
        - kind: "delta_s_p95_over"
          value: 0.15
        - kind: "coverage_under"
          value: 0.60
        - kind: "lambda_flip_rate_over"
          value: 0.20
        - kind: "error_rate_over"
          value: 0.01
      soft:
        - kind: "latency_p95_over_budget"
        - kind: "cost_per_solved_over_budget"

Server side evaluation snippet

def pass_gates(metrics, targets):
    if metrics["ds_median"] > targets["delta_s"]["median_max"]: return False
    if metrics["ds_p95"]    > targets["delta_s"]["p95_max"]:    return False
    if metrics["coverage"]  < targets["coverage_min"]:          return False
    if not metrics["lambda_convergent"] and targets["lambda_convergent"]: return False
    if metrics["latency_p95_uplift"] > targets["latency_p95_uplift_max"]:  return False
    if metrics["error_rate"] > targets["error_rate_max"]: return False
    if metrics["tool_success"] < targets["tool_success_min"]: return False
    if metrics["cost_uplift"] > targets["cost_uplift_max"]: return False
    return True

Observability you must log

Exposure events with assignment keys and sticky sampling id.
Version pins on every request.
ΔS, coverage, λ states, latency, error, tool success, cost.
Gate decisions and the reason for pass or fail.
Abort triggers with the exact rule that fired.

Symptom to fix map

Symptom	Likely cause	Open this
Users bounce between arms	no sticky sampling or client only flags	staged_rollout_canary.md
Mixed answers across versions	cache keys not partitioned by version pins	cache_warmup_invalidation.md
Quality regresses after graduation	pins drifted or alias flipped mid window	version_pinning_and_model_lock.md, blue_green_switchovers.md
429 storm during ramp	retry stampede or no global cap	rate_limit_backpressure.md
Tool loops under the flag	side effects not fenced	idempotency_dedupe.md

Common pitfalls

Client toggles treated as truth. Always evaluate on the server with signed manifests.
Two versions live without cache namespace separation.
Region flags diverge and never converge. Roll by region with audit hashes.
Graduation without a pinned baseline or a stable eval window.

🔗 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 based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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