Automated Ticket Triage with Observability Context

From Inbox Chaos to Clear Priorities

Traditional queues treat every alert like a ticket—and operators pay the price. With observability data (logs, metrics, traces) fused into each event, AI agents can auto-classify issues by impact, affected services, and SLA risk. The result is a ranked queue that surfaces what matters now and suppresses noisy duplicates.

Context-Aware Routing that Saves Minutes (and Incidents)

Context is everything: dependency maps, recent deploys, blast radius, and user impact. Agents enrich tickets with this evidence and route them to the right team on the first try—L3 for a core backbone flap, SRE for a rollout regression, security for abnormal egress. Fewer handoffs, faster ownership, tighter MTTR.

Continuous Learning from Outcomes

Every resolved ticket becomes training data. Agents learn which signals predicted severity, which fixes worked, and which routes avoided escalation. Over time, teams see fewer false positives, slimmer queues, and measurable gains in SLA adherence—without adding headcount.

Ready to turn noisy queues into prioritized, context-rich work?

Observeasy uses observability-driven AI agents to auto-triage, enrich, and route tickets—cutting MTTR and escalation churn.

👉 Book a demo and see intelligent triage in action.