Operations Guide
AI Error Tracking Guide (2026) - Reliability Engineering
AI error tracking: classify errors (4xx vs 5xx), track error rates, analyze root causes, implement prevention strategies, and maintain error dashboards.
Direct answer
AI error tracking: classify errors (4xx vs 5xx), track error rates, analyze root causes, implement prevention strategies, and maintain error dashboards.
Fast path
- Error classification: 4xx (client errors), 5xx (server errors), timeouts, retries.
- Error rate monitoring: track error rate percentage, alert on threshold breach.
- Root cause analysis: investigate error patterns, identify systemic causes.
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Implementation Steps
- Error classification: 4xx (client errors), 5xx (server errors), timeouts, retries.
- Error rate monitoring: track error rate percentage, alert on threshold breach.
- Root cause analysis: investigate error patterns, identify systemic causes.
- Prevention strategies: implement fallbacks, circuit breakers, input validation.
- Dashboard design: real-time error rate, error types breakdown, historical trends.
Frequently Asked Questions
How to classify AI API errors?
Classify AI API errors: 400 bad request (invalid prompt), 401 unauthorized (key issue), 403 forbidden (permission), 429 rate limit (quota exceeded), 500 server error (provider issue), timeout (slow response). Track each type separately for targeted fixes.
What error rate threshold for AI?
AI error rate thresholds: <1% error rate normal operation, >1% triggers investigation, >5% triggers incident response. Different thresholds per error type: 429 (rate limit) acceptable during high load, 500 (server error) requires immediate action.
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