AI-Native Observability: Self-Healing Infrastructure Through Closed-Loop Remediation Driven by Foundation Models

Abstract

The convergence of foundation models with observability platforms is enabling a new class of AI-native infrastructure that can detect, diagnose, and remediate operational anomalies autonomously within defined safety envelopes. This paper presents LoopGuard, a closed-loop remediation system that couples a fine-tuned foundation model with live telemetry streams, a policy-constrained action space, and a mandatory human escalation gate for high-blast-radius actions. Evaluated over 10 months across two large-scale SaaS platforms processing over 50 million daily active users, LoopGuard autonomously resolved 61% of P2-severity incidents without human intervention, reducing median MTTR from 38 minutes to 9 minutes. Safety analysis across 1,240 autonomous remediations recorded zero incidents of incorrect high-impact actions. Governance frameworks for production deployment of closed-loop AI remediation systems are proposed.