---
name: al-639-cut-it-alert-noise-before-anyone-gets-paged-let-an-ai-ops
description: "UK Tech News (Nov 4, 2026) reports early results from UK managed services provider ANS using LogicMonitor's Edwin AI since August 2026: AI event correlation connects related alerts, surfaces root-cause context and recommends next steps. ANS reports 83% less alert noise, observability incident volumes down from about 4,300 in July to 2,383 by Oct 29 (it notes seasonal variation too), 44% faster average resolution and SLA performance above 97%. It is also testing connecting its own internal AI agent to LogicMonitor data through MCP (in private preview) for natural-language investigation, while keeping human oversight of critical operational decisions. Figures are company-reported. Use when the user wants help with alert correlation and root-cause context (it & internal help) for a small business."
---

# Cut IT alert noise before anyone gets paged: let an AI ops agent group related alerts into one incident, attach root-cause context and suggest the next step, while engineers still approve every fix

UK Tech News (Nov 4, 2026) reports early results from UK managed services provider ANS using LogicMonitor's Edwin AI since August 2026: AI event correlation connects related alerts, surfaces root-cause context and recommends next steps. ANS reports 83% less alert noise, observability incident volumes down from about 4,300 in July to 2,383 by Oct 29 (it notes seasonal variation too), 44% faster average resolution and SLA performance above 97%. It is also testing connecting its own internal AI agent to LogicMonitor data through MCP (in private preview) for natural-language investigation, while keeping human oversight of critical operational decisions. Figures are company-reported.

## Inputs to collect first
Ask the user for anything below that they haven't already given you. Don't guess missing facts.

- The material the task is about (pasted text, notes or data)

## Instructions

Do the task below with the user's inputs in place of each [bracket].

1. Export one month of alerts and incidents from your monitoring tool; count how many alerts became real incidents.
2. Ask an AI assistant to cluster the alerts by host, service, time window and dependency, and propose correlation rules ('these 12 alerts within 5 minutes = one incident').
3. Turn on your monitoring platform's AI correlation (or apply the rules) in shadow mode for two weeks: compare its grouped incidents with what your engineers actually opened.
4. For each grouped incident, have the agent attach likely root cause, recent changes and the runbook step it suggests, marked as a suggestion.
5. Go live only for incident grouping and context; engineers still approve every remediation. Track alert volume, incidents opened, time to resolve and SLA each week.

## Safety checks (share these with the user)

- Check names, dates, numbers and any claim against your own source material before you use the answer. AI can misread or invent details.
- Say clearly which parts need a person to review or approve before anything is sent, paid, posted or filed.

## About this skill

- Works with: LogicMonitor Edwin AI or similar AIOps (event correlation), monitoring/observability platform, ITSM tool, optional MCP connection for your own agent
- Based on this real-world example: https://uktechnews.co.uk/2026/11/04/ans-cuts-it-alert-noise-by-83-with-logicmonitor-edwin-ai/
- Free prompt from AgentPlaybook Real Estate: https://agentic-library.netlify.app/prompt/al-639-cut-it-alert-noise-before-anyone-gets-paged-let-an-ai-ops-ag/
- To install in an agent that supports Agent Skills, save this file as SKILL.md in a folder with the same name as `name` above (for example in your skills directory). Install steps for Claude.ai, Claude Code, Cursor and Codex: https://agentic-library.netlify.app/skills/