FROM AIOPS TO AGENTOPS: SERVICE-LEVEL OBJECTIVES AND ERROR-BUDGET-GOVERNED AUTONOMY FOR PRODUCTION AI AGENTS

Abstract

Large language model (LLM) agents now run production workflows, including IT and network operations, yet they are usually monitored with signals designed for deterministic services: availability, latency, and error rate. An agent can satisfy all three while completing the wrong task. This paper extends AIOps-style service management to agentic systems. It contributes a layered set of service-level indicators (SLIs) covering platform health, step-level tool use, verified task outcomes, and guardrail integrity; a taxonomy of seven agent failure classes mapped to the signals that expose them; and an error-budget policy that governs an agent's autonomy as well as its release cadence. A Monte Carlo simulation of a production operations agent evaluates the approach across 2,100 injected incidents. Conventional platform monitoring detected only platform failures, firing in at most 1.7% of runs for the other six classes. Burn-rate alerting on verified task outcomes detected every class in 89.7% to 100% of runs within 24 hours, with no false alarms, but took a median of 3.5 to 10.6 hours under standard thresholds. A change-aware rule, active for six hours after a deployment or model change, cut median detection of change-induced regressions to 44 to 63 minutes at a cost of about one additional page per month. The results support layered, outcome-based service-level objectives as the operational basis for production agents.