AI Reliability & Observability (AIRE) Framework

Q: What is the AIRE framework?

AIRE (AI Reliability Engineering) is a framework for production AI reliability covering: Availability (uptime SLOs), Integrity (output quality), Resilience (failure handling), and Efficiency (cost-performance optimization).

Q: How do you monitor LLM performance in production?

Monitor: response latency (P50/P95/P99), token throughput, error rates, output quality scores (via automated evaluators), hallucination detection metrics, user feedback signals, and cost per query over time.

Q: What causes AI model drift?

Model drift occurs when: input data distributions change (data drift), the relationship between inputs and outputs changes (concept drift), user behavior evolves, or the real-world domain shifts. Regular monitoring and retraining are essential.

Q: How do you set up AI incident response?

Define severity levels (model degradation vs. full outage), automate drift detection alerts, maintain model rollback procedures, keep fallback models ready, document runbooks for common AI failures, and conduct post-incident reviews.

Satyam Kumar

Back to Blog

AI Architecture

AI Reliability & Observability (AIRE) for Production Systems

February 19, 202637 min read

AI reliability AI observability AIRE framework LLM monitoring AI drift detection production AI monitoring

Frequently Asked Questions

Share this article

Twitter LinkedIn WhatsApp

Satyam

AI & Cloud Architect. Helping teams build systems that scale to millions.

AI Reliability & Observability (AIRE) for Production Systems

Frequently Asked Questions

Share this article

Comments

Leave a comment