AI Feature Rollout And Experimentation

AI Feature Rollout and Experimentation

Operating contract

Inputs

Outputs

• Produce: rollout plan, allocation rules, experiment analysis, rollback triggers, and release decision.

Capability and permission boundaries

Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.

Degraded mode

When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.

Decision rules

Anti-Patterns

• Treating absent evidence as success. Fix: mark the check unassessed and name the missing source.
• Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action.
• Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit.
• Reporting a metric without population, window, or source. Fix: attach all three.
• Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner.

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Launching a new AI feature or a major prompt/model change.
• A/B testing two prompts or two models on live traffic with statistical rigor.
• Rolling out gradually by tenant tier, region, or cohort.
• Adding tenant-level opt-out/consent surfaces required by enterprise procurement.
• Automating rollback when an SLO or eval signal degrades.

Do Not Use When

• The task is the eval harness itself — ai-eval-harness.
• The task is product-level prompt design — ai-prompt-engineering.
• The task is generic feature flagging unrelated to AI — use your flag platform docs.

Required Inputs

• Feature-flag platform (LaunchDarkly, Statsig, ConfigCat, Unleash, in-house).
• Eval harness producing per-feature, per-variant metrics.
• SLO + error budget signals (ai-hallucination-slo-and-grounding).
• Tenant tier + consent state.

Workflow

1. Read this SKILL.md.
2. Define the rollout taxonomy (§1) — internal → dogfood → canary → tier ramp → GA.
3. Wire flags + targeting (§2) — per-tenant, per-user, per-cohort, per-region.
4. Build shadow-mode capability (§3) for risky changes.
5. Implement A/B + multivariate evaluation (§4) with eval harness integration.
6. Wire auto-rollback on signal degradation (§5).
7. Provide tenant-level opt-out and consent (§6).
8. Document release runbook (§7).
9. Apply anti-patterns (§8).

Quality Standards

• No AI prompt or model change goes to GA without staged rollout.
• Every variant has a primary metric and a guardrail metric.
• Auto-rollback triggers in < 10 minutes after a guardrail breach.
• Tenant opt-out is honoured at the gateway, not the UI.
• Consent is captured per tenant per AI feature class; recorded with timestamp + actor.
• Every rollout has a release log entry with start time, stages, metrics, decisions.

Anti-Patterns

• One global flag → on for everyone. No early-warning blast radius.
• A/B with one metric and no guardrail. Optimises one thing, breaks another.
• Manual rollback only. Slow at 3am.
• Tenant opt-out implemented as a UI hint that doesn't actually disable the model call.
• Shadow-mode that compares only on a tiny sample. Confidence is illusion.
• "Soft launch" with no rollout plan. Six tenants find a critical bug at the same time.

Outputs

• Rollout taxonomy specification.
• Flag and targeting policy.
• Shadow-mode runner code.
• A/B experiment design template.
• Auto-rollback rules and alert wiring.
• Tenant consent / opt-out surface.
• Release runbook template.

Evidence Produced

References

• Companion: ai-eval-harness, ai-hallucination-slo-and-grounding, ai-cost-per-tenant-attribution, ai-on-saas-architecture, ai-observability-and-debugging, deployment-release-engineering, saas-entitlements-and-plan-gating.
• Incident handoff: an auto-rollback is the opening of an incident, not a silent revert. The rollback action must (a) flip the flag, (b) open an incident in the incident tracker with failure_class_hint derived from the breaching guardrail, (c) page the AI on-call. See ai-incident-detection-and-triage for severity, ai-incident-response-runbook for the playbook, and ai-incident-recovery-and-rollback for the eval-gated re-promotion path that brings the new variant back safely.

§1 Rollout Taxonomy

Five stages, each with entry/exit criteria.

Per-tenant overrides allow flagship customers to opt into earlier stages.

§2 Flags + Targeting

Flag identity:

ai.<feature>.<change-id>           # e.g., ai.support-copilot.prompt-v18

Targeting rules in order:

1. Per-tenant force-on / force-off list (operator override).
2. Per-tenant consent state — must be opt-in for opt-in features.
3. Stage cohort (Internal / Dogfood / Canary / Tier).
4. Region (if regional gating).
5. Percentage rollout within cohort.

Resolved at the gateway before binding lookup so the binding (model, prompt version) can be variant-specific.

§3 Shadow Mode

Risky changes (new model, new prompt, new retrieval) ship first in shadow:

• Real traffic goes to the existing variant; the new variant runs in parallel.
• Both outputs scored (judge-LLM) and stored.
• No user-facing change.

Implementation: gateway, after producing the primary response, asynchronously calls the shadow variant; logs both. Pairs are scored offline.

Promote shadow → canary when distribution metrics match within tolerance and judge-LLM prefers the new variant on ≥ 55% of pairs (with significance).

§4 A/B + Multivariate

Design template:

experiment_id: support-copilot.prompt.v18-vs-v17
hypothesis: "v18 improves faithfulness by ≥ 2pp without regressing latency"
metric:
  primary: faithfulness                       # higher is better
  primary_min_uplift: 0.02
  primary_significance: 0.05
guardrails:
  - name: latency_p95
    threshold: { max: 3500 }
  - name: cost_per_generation
    threshold: { max: 0.02 }
  - name: abstain_rate
    threshold: { max: 0.10 }
audience:
  stages: [canary, tier:starter, tier:pro]
  region: any
allocation: { v17: 50, v18: 50 }
min_sample: 5000 per variant
max_duration: 14 days

Decision after min_sample is met:

• Primary uplift significant AND no guardrail breach → promote v18.
• Primary uplift not significant → hold v17.
• Guardrail breach in any window → rollback v18 immediately.

Use a sequential-testing-aware library (e.g., Bayesian or sequential alpha-spending) — peeking penalty matters for online AI evals.

§5 Auto-Rollback

Rules engine that watches a variant's metrics:

- variant: support-copilot.prompt-v18
  rules:
    - condition: faithfulness_1h < 0.92
      action: pause_rollout
    - condition: faithfulness_6h < 0.90
      action: rollback
    - condition: abstain_rate_1h > 0.20
      action: rollback
    - condition: cost_p95_1h > 0.05
      action: pause_rollout
    - condition: ai.injection.suspected_rate_1h > baseline*3
      action: rollback

pause_rollout stops ramping; rollback flips the flag back. Both record an event and an in-product notice (if visible).

§6 Tenant Consent and Opt-Out

Two contracts:

• Opt-in features (e.g., agent that takes actions, training-data contributions): tenant admin explicitly toggles on. Default off.
• Opt-out features (e.g., the support copilot itself): default on; tenant admin can turn off.

Schema:

CREATE TABLE tenant_ai_consents (
    tenant_id   BIGINT UNSIGNED NOT NULL,
    feature_class VARCHAR(64) NOT NULL,       -- 'agent', 'training_data', 'support_copilot'
    state       ENUM('opt_in','opt_out','unset') NOT NULL,
    actor_user_id BIGINT UNSIGNED,
    set_at      DATETIME NOT NULL,
    PRIMARY KEY (tenant_id, feature_class)
);

Gateway reads consent before honoring flags. Consent state is independent of entitlement state (Enterprise plan with consent = unset → still off until set).

§7 Release Runbook

Per change, a markdown doc:

# Rollout: support-copilot prompt v18

Owner: @pb
Start: 2026-05-12 09:00 UTC
Stages: Internal → Dogfood → Canary → Pro → GA
Variant: support-copilot.prompt-v18 vs v17
Hypothesis: ...
Risk: ...
Rollback plan: flag flip; LD link

## Log
- 2026-05-12 09:00 — Internal 100%
- 2026-05-13 09:00 — Dogfood 100%; faithfulness +1.8pp; no guardrails
- 2026-05-15 09:00 — Canary acme/globex; faithfulness +2.1pp
- ...

§8 Anti-Patterns

• "We flipped it for everyone." No diagnostics on regression.
• Auto-rollback rule that triggers on a single bad minute. Noise → flapping.
• Shadow comparisons that include tenants who consented to v17 only.
• Opt-in features turned on by default for trial tenants. Procurement disaster.
• Consent stored only in the marketing tool, not in the gateway. Honoured nowhere.
• One experiment per quarter — culture problem.
• No release log; next person can't reason about why the prompt is what it is.

§9 Read Next

• ai-eval-harness — produces the metrics.
• ai-hallucination-slo-and-grounding — guardrail signals.
• ai-cost-per-tenant-attribution — cost guardrail.
• ai-observability-and-debugging — investigation in flight.
• deployment-release-engineering — broader release discipline.
• saas-entitlements-and-plan-gating — overlap with entitlements.