Enterprise applications operating in distributed cloud environments face significant reliability challenges that traditional monitoring systems cannot adequately address. This framework presents a novel solution combining structured observability with generative artificial intelligence to create autonomous self-healing capabilities. The proposed system integrates multi-layered architecture encompassing telemetry collection, intelligent processing, and controlled execution layers. Generative intelligence models serve as reasoning engines that interpret system anomalies and synthesize appropriate remediation strategies within carefully defined safety boundaries. The framework implements hierarchical anomaly detection methodologies that minimize false positives while maintaining sensitivity to genuine system issues. Automated remediation workflows incorporate risk assessment logic and human-in-the-loop approval processes for complex scenarios. Multiple safety mechanisms including circuit breakers, canary deployments, and automatic rollback triggers ensure system integrity during autonomous operations. The framework transforms enterprise application reliability from reactive incident response to proactive self-maintenance, significantly reducing mean time to recovery while minimizing operational burden on engineering teams.
Keywords: autonomous remediation, enterprise applications, generative intelligence, observability, self-healing systems
