# Alert Noise Reduction Agent

> An alert-tuning agent that scores alerts by actionability, dedupes, and time-box-suppresses proven noise — never silencing an alert tied to a real incident.

- **Category:** DevOps & SRE
- **Difficulty:** Advanced
- **Setup time:** 45 min
- **Stacks:** Claude, LangGraph, OpenAI
- **Version:** 2.0.0 (updated 2026-06-21)
- **Source:** https://agent-kits.com/kit/alert-noise-reducer

## Overview

Score → correlate → tune → suppress (carefully): turns a noisy alert stream into a quieter, still-trustworthy one.

Actionability-based: it ranks alerts by how often they're actually acted on or tied to incidents, not by volume alone.

Recommends concrete tuning (thresholds, grouping, dedup) and only time-box-suppresses alerts proven to be noise.

Defensive: never suppresses an alert ever linked to a real incident, never auto-touches critical/SEV1 alerts, and keeps every action reversible and audited.

## When to use it

- On-call is drowning in alerts and real signals are getting lost in the noise.
- You have alert history (fire/ack/incident-correlation) the agent can score actionability from.
- You want data-backed tuning recommendations and safe, reversible suppression of proven noise.
- You want to cut fatigue while keeping a hard guarantee that incident-linked and critical alerts are never silenced.

## When not to use it

- You lack alert/incident history, so actionability can't be measured — suppression would be blind.
- You expect it to autonomously silence critical-service alerts; those are recommendation-only.
- Your 'noisy' alerts are actually under-investigated real signals.
- You can't keep suppression reversible, time-boxed, and audited.

## Architecture

- **Alert-stream intake** — Ingests the alert stream/inventory with metadata (service, severity, owner) for analysis over a configurable window.
- **History & incident correlation** — Pulls each alert's fire/ack history and — critically — whether it has ever correlated with a real incident, the hard gate on suppression.
- **Actionability scoring** — Scores each alert by how often it's actually acted on (ack rate, time-to-ack) versus pure firing volume, surfacing chronically non-actionable ones.
- **Correlation & dedup** — Groups related/duplicate alerts so a single root cause doesn't page ten times, while preserving visibility into the underlying alerts.
- **Tuning & suppression gate** — A deterministic gate forbids suppressing incident-linked or critical-service alerts; only proven, non-critical, never-incident-linked noise can be time-box suppressed.
- **Recommendation & PR** — Produces concrete tuning (thresholds, grouping rules) — often as a config PR — and applies bounded, reversible suppression where allowed.
- **Audit & review** — Logs every suppression with its evidence, expiry, and scope, and surfaces a review so humans can see exactly what was quieted and why.

## Files in this bundle

- `system-prompt.md` — the agent's system prompt
- `tools.json` — tool/function definitions the agent expects
- `workflow.md` — step-by-step run loop
- `.env.example` — environment variables to fill in
- `kit.json` — full structured metadata
- `starters/` — minimal starter snippets
- `LICENSE` — Apache-2.0 (code, configuration, and schema)
- `LICENSE-CONTENT` — CC BY 4.0 (specification text and documentation)
- `NOTICE` — attribution and trademark notice
- `run.py` — **runnable demo**: real agent loop with mock tools (`pip install anthropic`, set `ANTHROPIC_API_KEY`, `python run.py`)
- `agentaz.json` / `agentaz.schema.json` — AgentAz™ governance spec + schema

## Implementation notes

- Make 'never suppress an incident-linked alert' an absolute, deterministic gate — a single past incident correlation permanently disqualifies an alert from suppression, no matter how noisy.
- Score by actionability (acks, time-to-ack, incident correlation), not raw volume; a frequent-but-always-acted-on alert is signal.
- Keep critical-service and SEV1-capable alerts recommendation-only and escalate decisions to humans.
- Make every suppression time-boxed, scoped, reversible, and audited — never an open-ended silence — and pair it with a tuning PR so the root cause gets fixed.
- Preserve visibility when deduping; collapsing distinct signals into one can hide a real problem.
- Backtest with 'suppressed-incident-linked alerts' as a hard zero metric before enabling any auto-suppression.
- The strong model earns its cost on the suppress-vs-tune judgment, while a cheaper model can pull and aggregate history.

## Variations

- **Basic — Noise analyzer:** Scores alerts by actionability, correlates duplicates, and recommends tuning with the supporting numbers for an SRE. No suppression.
- **Advanced — Guarded auto-suppression:** Adds time-boxed, reversible suppression of proven non-critical noise and tuning PRs, with the absolute incident-linked and critical-service guardrails enforced.
- **Enterprise — Org-wide alert hygiene:** Adds multi-team alert inventories, monitoring-as-code PR workflows, suppression audit and auto-expiry, on-call load analytics, and tuning from outcomes — incident-linked alerts always protected.

## FAQ

**Could it silence an alert that matters?**

No — that's the hard guarantee. Any alert that has ever correlated with a real incident is permanently ineligible for suppression, and critical-service alerts are recommendation-only. It can only auto-suppress proven, never-incident-linked, non-critical noise.

**How does it decide what's noise?**

By actionability, not volume: fire count, ack rate, time-to-ack, and incident correlation over a window. A high-volume alert that's consistently acted on is treated as signal, not noise.

**Is suppression permanent?**

No. Every suppression is time-boxed (auto-expires), scoped to the specific alert, reversible, and logged — and it's paired with a tuning recommendation/PR so the underlying noise actually gets fixed.

**What does it do with critical-service alerts?**

It never auto-suppresses or auto-tunes them. It surfaces recommendations and escalates the decision to on-call, because missing a real issue on a critical service is far costlier than the noise.

**Will deduping hide separate problems?**

It groups related/duplicate alerts while preserving visibility into the underlying ones, so a single root cause stops paging ten times without collapsing genuinely distinct signals.

**How do we trust it before going live?**

Backtest it on your alert history; the key check is that it would have suppressed zero incident-linked alerts. Start in advise mode (recommendations + tuning PRs) and enable auto-suppression for non-critical proven noise only once that holds.


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© 2026 AgentKits · www.agent-kits.com

**Open source.** Code, configuration, and the AgentAz™ schema are licensed under
**Apache-2.0**; specification text and documentation under **CC BY 4.0** (with
attribution to AgentKits, link to https://www.agent-kits.com). See LICENSE, LICENSE-CONTENT,
and NOTICE in this bundle. "AgentAz" and "AgentKits" are trademarks of AgentKits.

**Flagship reference blueprint.** View the source, AgentAz™ spec, and frozen v1.0 schema
in the public registry: https://github.com/agent-kits/agentaz
Original: https://agent-kits.com/kit/alert-noise-reducer
