AI Monitoring & Alerting: Proactive Incident Detection

Incident Detection Workflow

AI-driven monitoring follows four stages from signal collection to incident resolution. Each maps to one or more of the skills above.

Stage 1: Metric Collection

The agent continuously queries your monitoring tools on a schedule. A typical collection prompt runs every two minutes: "Query Datadog for error rate, p95 latency, and request volume on the payment service for the last 10 minutes. Also pull the current alert state from Grafana for all panels tagged payment-service." This gives the agent a real-time view of system health without relying on static threshold rules configured months ago.

The agent also queries Sentry at the same interval: "Fetch all new error events in the last 10 minutes with severity Error or Fatal. For each event, include the affected release version, error frequency, and user impact count." This correlates code-level errors with infrastructure metrics from the first moment of detection.

Stage 2: Anomaly Detection

Rather than comparing a metric against a fixed threshold, the agent reasons about the data in context. It compares the current reading against the baseline for this time of day, this day of week, and this week relative to recent release activity. A 30% increase in error rate is alarming if it started exactly when the last deploy landed; it is expected noise if it occurs every Monday morning when batch jobs run.

The agent also performs cross-signal correlation: if both the Datadog p95 latency and the Sentry database error count spike simultaneously, the agent identifies the database as the likely root cause rather than treating them as two separate incidents. This correlation, which would take a human engineer several minutes of dashboard switching to perform, happens in a single agent reasoning step.

Stage 3: Alert Routing

When an anomaly meets the severity threshold, the agent routes the alert through two channels simultaneously. For P1 incidents, the PagerDuty skill creates an incident with the structured diagnosis in the description and triggers the on-call escalation policy for the affected service. The Slack MCP posts to the #incidents channel with a formatted message that includes: the affected service, the anomaly description, the likely root cause, the associated Sentry error events, the Datadog metric graph link, and the recommended first diagnostic action.

For P2 and P3 incidents, the agent posts to a lower-priority Slack channel without paging PagerDuty, giving the on-call engineer visibility without waking them up for non-critical issues.

Stage 4: Incident Response

Once the on-call engineer acknowledges the PagerDuty incident, the agent continues in the background: monitoring whether the metrics are trending toward recovery or worsening, posting updates to the incident Slack thread every five minutes, and flagging when new Sentry error types emerge that suggest the incident has spread to additional services. When the incident resolves, the agent marks the PagerDuty incident as resolved and drafts a post-mortem outline with the full incident timeline pre-populated.

Step-by-Step Setup

The following configuration sets up Sentry MCP and Slack MCP as a minimal monitoring stack. Add Datadog, Grafana, and PagerDuty skills as your infrastructure coverage expands.

Step 1: Add Skills to Your MCP Config

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-sentry"],
      "env": {
        "SENTRY_AUTH_TOKEN": "your_sentry_auth_token",
        "SENTRY_ORG": "your-org-slug"
      }
    },
    "slack": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-slack"],
      "env": {
        "SLACK_BOT_TOKEN": "xoxb-your-slack-token",
        "SLACK_TEAM_ID": "T0XXXXXXXX"
      }
    },
    "datadog": {
      "command": "npx",
      "args": ["-y", "mcp-server-datadog"],
      "env": {
        "DD_API_KEY": "your_datadog_api_key",
        "DD_APP_KEY": "your_datadog_app_key",
        "DD_SITE": "datadoghq.com"
      }
    }
  }
}

Step 2: Verify Each Connection

• "Show me the top 5 unresolved issues in my Sentry project" — verifies Sentry MCP
• "Post a test message to #monitoring-test" — verifies Slack MCP
• "Query the request rate metric for my main service in the last hour" — verifies Datadog skill

Step 3: Set Up Your First Monitoring Prompt

"Every 5 minutes, check Sentry for new Fatal or Error
events on the production environment. If any new error
type appeared in the last 5 minutes with more than
10 occurrences, post an alert to #incidents with:
- Error name and message
- Affected release version
- Number of affected users
- Link to the Sentry issue
- Recommended first diagnostic step"

Step 4: Add PagerDuty and Grafana for Full Coverage

Add PagerDuty skill to automate on-call paging for P1 alerts, and Grafana skill to include dashboard panel context in your alert messages. Configure each with the appropriate API token following the same MCP config pattern above.

Comparison Table

Use this table to understand which skill covers each layer of the monitoring stack and the key trade-offs between observability platforms.

Frequently Asked Questions