AI filters, prioritizes, and enriches system alerts before sending. Your team gets actionable notifications, not alert fatigue.
Alert fatigue is a real operational risk. Monitoring systems generate hundreds of notifications daily, and teams learn to ignore them. When a genuinely critical alert fires, it gets buried in the noise. The root cause isn't too many alerts — it's that alerts lack prioritization, context, and intelligent filtering.
MultiMail's AI agent sits between your monitoring systems and your team's inbox. It evaluates each alert's severity, enriches it with contextual data (recent deployments, related metrics, historical patterns), and sends only actionable notifications. Autonomous oversight ensures zero delay on critical alerts.
Your monitoring system (Datadog, Prometheus, CloudWatch, etc.) sends metric threshold events to the AI agent via webhook when values exceed configured limits.
The AI evaluates each alert's severity based on the metric, threshold breach magnitude, duration, and whether related metrics are also anomalous. It filters transient spikes from sustained issues.
The agent adds context: recent deployments, correlated metrics, historical patterns for this metric, and suggested investigation steps. This turns a raw alert into an actionable notification.
Critical alerts are sent immediately via send_email. Lower-priority alerts are batched into digest emails. The agent adjusts recipients based on severity and on-call schedule.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def process_alert(alert: dict):
"cm"># Evaluate severity
severity = evaluate_severity(alert)
if severity == "noise":
return "cm"># Filter transient spikes
"cm"># Enrich with context
context = {
"recent_deploys": get_recent_deployments(),
"related_metrics": get_correlated_metrics(alert["metric"]),
"history": get_metric_history(alert["metric"], days=7)
}
body = (
f"[{severity.upper()}] {alert[&"cm">#039;metric']} on {alert['host']}\n\n"
f"Current value: {alert[&"cm">#039;value']}\n"
f"Threshold: {alert[&"cm">#039;threshold']}\n"
f"Duration: {alert[&"cm">#039;duration']}\n\n"
f"Context:\n"
f" Recent deploys: {context[&"cm">#039;recent_deploys']}\n"
f" Related metrics: {context[&"cm">#039;related_metrics']}\n\n"
f"Suggested actions:\n{suggest_actions(alert, context)}"
)
recipient = get_oncall_email(severity)
requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": recipient,
"subject": f"[{severity.upper()}] {alert[&"cm">#039;metric']} at {alert['value']} - {alert['host']}",
"text_body": body
}
)Receive monitoring alerts, add context, and send enriched notifications.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def send_alert_digest(alerts: list):
"""Send a digest of low-priority alerts every 30 minutes."""
if not alerts:
return
summary = f"Alert Digest: {len(alerts)} notifications\n\n"
for alert in alerts:
summary += (
f"- [{alert[&"cm">#039;severity']}] {alert['metric']} = {alert['value']} "
f"on {alert[&"cm">#039;host']} ({alert['duration']})\n"
)
summary += f"\n{len(alerts)} alerts in the last 30 minutes."
requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": "[email protected]",
"subject": f"Alert Digest: {len(alerts)} notifications",
"text_body": summary,
"html_body": build_digest_html(alerts)
}
)Consolidate non-critical alerts into a periodic digest email.
"cm">// Using MultiMail MCP tools for system alerts
async function processAlert(alert: MetricAlert) {
const severity = evaluateSeverity(alert);
if (severity === "noise") return;
const context = await gatherContext(alert);
const recipient = getOncallEmail(severity);
"cm">// Send enriched alert
await mcp.send_email({
to: recipient,
subject: `[${severity.toUpperCase()}] ${alert.metric} at ${alert.value} - ${alert.host}`,
text_body: [
`${alert.metric} on ${alert.host}`,
`Current: ${alert.value} | Threshold: ${alert.threshold}`,
`Duration: ${alert.duration}`,
``,
`Context:`,
` Recent deploys: ${context.recentDeploys.join(", ")}`,
` Related anomalies: ${context.relatedMetrics.join(", ")}`,
``,
`Suggested actions:`,
...context.suggestedActions.map(a => ` - ${a}`)
].join("\n")
});
}Process system alerts using MultiMail MCP tools.
AI filters transient spikes and noise, sending only actionable alerts. Your team trusts their inbox again because every alert they receive matters.
Every alert includes recent deployments, correlated metrics, and suggested actions. Engineers start investigating immediately instead of spending 10 minutes gathering context.
Critical alerts go to on-call engineers immediately. Lower-priority alerts are batched into digests. The right person gets the right alert at the right time.
Autonomous mode ensures critical system alerts are delivered instantly. When your database CPU is at 95%, there's no time for an approval queue.
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