Clarifying questions
- When and where are failures observed (region, environment: prod/staging, mobile/web/API)?
- How is “unreliable” defined (login errors, timeouts, auth latency, rate limits, user lockouts)?
- Recent changes or deployments correlated with failures?
- Which auth stack: SSO/OAuth/OIDC, LDAP, custom DB, MFA, third-party IdP?
- Error rates from logs/monitoring and SLO/SLA targets?
- Expected business impact / priority and acceptable outage window for fixes?
- Any compliance or audit constraints (2FA enforced, logging retention)?
Measurable acceptance criteria & metrics
- Reduce login failure rate from X% to ≤Y% over 7 days (example: from 2% → ≤0.2%)
- Median auth latency ≤300ms, 95th percentile ≤1s
- Successful login rate ≥99.8% per region
- No increase in security incidents; pass authentication integration tests and chaos test for 1 week
- Dashboard: error rate, latency, auth backend error breakdown, user-facing incidents
MVP vs Full solution
- MVP: Add guards and retries + circuit breaker for transient IdP errors, improved monitoring/alerts, config tweak (connection pool, timeouts), rolling redeploy to adjust backoffs. Quick rollbackable.
- Full: Architectural fixes — introduce caching of tokens, horizontal scaling of auth service, resilient queueing for async flows, multi-region IdP failover, automated canary deploys, comprehensive test harness, SLOs + remediation runbooks.
Rollback plan
- Deploy via canary (1% → 10% → 100%) with automatic metric gates (error rate, latency). If regressions exceed thresholds, immediately:
- Abort promotion and roll back canary to previous version
- If already at 100%, trigger automated rollback pipeline to last known-good image
- Notify on-call, open incident with runbook, revoke any config changes via feature flags
- Post-mortem and preserve artifacts (logs, traces) before reattempt
This plan emphasizes measurables, low-risk incremental change, observability, and a clear rollback path.
