Feature Flag AI

Goal

Safely ship AI capabilities with controlled exposure and fast rollback.

Flag Model

{
  "flag": "ai_summarization_v2",
  "enabled": false,
  "rollout": {
    "percentage": 5,
    "strategy": "sticky-user",
    "segments": ["internal", "beta-customers"]
  },
  "kill_switch": true,
  "expires_on": "2026-12-31"
}

Rollout Stages

Runtime Guard

if (!flags.isEnabled('ai_summarization_v2', userContext)) {
  return fallbackSummary(text);
}
return aiSummary(text);

Promotion Rules

• Promote only if:
• Quality >= baseline + 3%
• Safety violations do not increase
• P95 latency within SLO
• Cost/request within budget

Rollback Rules

• Trigger immediate disable if:
• Safety incident severity high
• Error rate exceeds threshold
• Budget spike beyond cap

Validation Checklist

Troubleshooting

Advanced Implementation Notes

Operational Guardrails

• Define measurable SLOs before rollout.
• Capture baseline metrics and compare deltas post-change.
• Add alert thresholds with explicit on-call ownership.
• Use environment-specific overrides for dev/staging/prod.

CI/CD and Validation Expansion

# Example verification sequence
npm run lint
npm test
npm run build

{
  "quality_gate": {
    "required": true,
    "min_score": 0.8,
    "block_on_failure": true
  }
}

Security and Compliance Checks

Performance and Cost Notes

• Budget requests and tokens per endpoint/class of workload.
• Profile p95 and p99 latency as separate objectives.
• Add caching only where correctness is preserved.
• Use periodic reports to catch drift in cost/quality.

Extended Troubleshooting