Why Reliability Matters

AI systems fail differently than traditional software:

• Models can return bad output
• API calls can timeout
• Costs can spiral unexpectedly

Failure Modes

1. Bad Output

Model produces incorrect or harmful output.

Mitigation:

• Output validation
• Human review for critical tasks
• Multiple model voting

2. API Failures

Provider downtime or rate limits.

Mitigation:

• Multiple provider fallback
• Circuit breakers
• Retry with backoff

3. Cost Overruns

Unexpected token usage.

Mitigation:

• Budget alerts
• Per-request limits
• Token metering

4. Latency Spikes

Slow responses.

Mitigation:

• Timeout limits
• Fallback to faster models
• Queue with SLA

Architecture Patterns

Fallback Chain

Primary (GPT-5) → Secondary (Claude 4) → Tertiary (GPT-4o Mini) → Human

Circuit Breaker

After N failures, stop calling the provider temporarily.

Rate Limiter

Limit requests per user/IP to prevent abuse.

Monitoring Essentials

• Request success rate
• Latency percentiles (p50, p95, p99)
• Cost per request
• Error types
• Quality metrics

Our Recommendations

1. Always have fallback — Never rely on single provider
2. Set budgets — Prevent runaway costs
3. Validate outputs — Don’t trust blindly
4. Log everything — Debugging AI is hard
5. Test failures — Chaos engineering for AI