AIOps
Operations that find problems early and fix the known ones safely
At a glance
AIOps turns monitoring data, tickets and runbooks into operations that find problems early and fix known ones safely. NGT builds the observability foundation first, then adds automation and agents step by step; engineers approve anything new or risky.
Logs, metrics, traces and SLOs come first: automation is only as good as its signals.
Known failures run approved runbooks; for new ones, AI gathers evidence and an engineer decides.
Specialised agents behind a super-agent, with guardrails, audit and human handover.
Known fixes run safely; new failures get evidence and a decision
Observability first, then automation and agents, one step at a time.
- Signals with one contextMetrics, logs, traces, events and changes, checked against baselines and SLOs.
- Known failures: approved runbooksRestart, resize or roll back, then verify; roll back the action if it fails.
- New failures: evidence firstAn AI agent searches runbooks and past incidents; an on-call engineer decides.
- Every approved fix is learnedFixes become rules, so the set of known failures grows.
Point at a component to light its links; select it for what it does here and the practice that makes it right on AWS.
Problems we solve
Four operations problems customers bring us, each with the architecture we use. Select one.
Routine requests swamp IT operations
Routine requests arrive all day, and change requests take hours to prepare by hand. Engineers spend their days on repeat work.
- A super-agent routes each request to a specialised agent
- Anomalies go straight to monitoring and runbook agents
- Registered tools only, behind an MCP gateway with audit
Agents carry the routine work; engineers approve risky steps and take the exceptions.
Point at a component to light its links; select it for what it does here and the practice that makes it right.
Explore AIOps
5 pages, each a few minutes to read.
Frequently asked questions
What customers ask first.
Automation and AI applied to operations data (metrics, logs, traces, tickets and changes) to find problems earlier, find causes faster and fix known problems safely.
At NGT it covers request and service management, change and operations automation, and proactive monitoring and incident support.
Yes. Automation is only as good as its signals. If telemetry is incomplete or alerts are noisy, we start with the observability foundation and alert hygiene.
Only for known failures with an approved runbook that can be verified and reversed. New or risky actions go to an engineer, with the evidence and a proposed fix.
Yes. Agents and automation plug into what you run, such as ServiceNow, Prometheus, Grafana, Datadog, Splunk or CloudWatch. They do not replace them.
With an assessment of monitoring, alert volume, ticket flow and runbooks, then a pilot on a few high-volume, low-risk use cases such as ticket or alert triage.
A routing layer sends each request to a suitable model, and more than one provider is supported. The choice follows your security and data-residency rules.
