Autonomous Observability: Ml-Optimized SLO/SLA Enforcement

Abstract

Modern distributed systems increasingly fail not because of insufficient instrumentation, but because observability itself has become operationally unmanageable at scale. Despite pervasive telemetry, enterprises continue to experience prolonged mean time to detection (MTTD), reactive service-level objective (SLO) breaches, alert fatigue, and governance blind spots particularly in multi-cloud, microservice, and AI-driven platforms. Existing observability approaches largely treat telemetry analysis, reliability enforcement, and governance as disconnected concerns, relying on static thresholds, manual tuning, and post-hoc remediation.

This paper introduces Autonomous Observability, a systemic framework that transforms observability from a passive monitoring function into an adaptive, learning-driven control plane for SLO/SLA enforcement. The proposed framework integrates machine learning–based signal interpretation, probabilistic risk modeling, and policy-constrained decision orchestration to continuously predict, prevent, and mitigate service degradation before contractual or reliability violations occur. Unlike prior work, our approach explicitly couples observability with closed-loop reliability control, incorporating human-in-the-loop governance, auditability, and safety boundaries by design.

We present a layered architecture that separates telemetry ingestion, learned behavioral models, risk-aware SLO controllers, and governance enforcement, enabling incremental enterprise adoption without vendor lock-in. Operational evaluation across representative enterprise workloads demonstrates measurable reductions in MTTD and MTTR, improved SLO adherence under workload drift, and significant decreases in operator toil. We also analyze failure modes, governance risks, and ethical considerations, positioning Autonomous Observability as a foundational capability for reliable, accountable, and scalable cloud-native systems.