AI Agent Reliability Engineering 2026: SLOs and Failure Modes

Traditional SRE metrics like HTTP uptime and latency fail to capture whether AI agents are actually doing useful work. A three-layer SLO framework is proposed: service-level reliability, output validity, and task success. Each layer fails independently and requires separate error budgets, with burn-rate alerting rather than total-burn tracking. Eight named failure modes are defined (model regression, tool failure, retrieval drift, prompt regression, schema drift, provider outage, cost spike, hallucination), each with distinct detection signals and runbook steps. Reliability drills, OpenTelemetry-based traces with agent-specific attributes, and postmortems that produce actionable followups round out the framework. Several traditional SRE practices — five-nines targets, pure synthetic monitoring, latency-only deployment gates — are identified as unsuitable for non-deterministic agent systems.