Log Monitoring with AI Agents: Intelligent Alerting & Analysis

Step-by-Step Setup

The following instructions set up Datadog MCP and Sentry MCP as your primary monitoring layer, then add PagerDuty Skill for incident escalation. Grafana Skill and Elastic Skill can be added independently for open-source observability stacks.

Step 1: Gather Your API Keys

Each monitoring platform requires an API key with appropriate read permissions. Before editing your MCP config, collect the following from your respective dashboards:

• Datadog API Key and Application Key (read-only scope)
• Sentry Auth Token (project:read, org:read scopes)
• PagerDuty REST API Key (read + write for incident creation)

Step 2: Add Servers to Your MCP Config

Open ~/.claude/settings.json (Claude Code) or .cursor/mcp.json (Cursor) and add the monitoring MCP servers:

{
  "mcpServers": {
    "datadog": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-datadog"],
      "env": {
        "DATADOG_API_KEY": "your_api_key",
        "DATADOG_APP_KEY": "your_app_key",
        "DATADOG_SITE": "datadoghq.com"
      }
    },
    "sentry": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-sentry"],
      "env": {
        "SENTRY_AUTH_TOKEN": "your_sentry_token",
        "SENTRY_ORG": "your-org-slug"
      }
    },
    "pagerduty": {
      "command": "npx",
      "args": ["-y", "mcp-server-pagerduty"],
      "env": {
        "PAGERDUTY_API_KEY": "your_pagerduty_key"
      }
    }
  }
}

Step 3: Restart and Verify Each Connection

After restarting your AI assistant, test each server with a lightweight query:

• "Show me the error rate for all services in the last 15 minutes" — verifies Datadog MCP
• "List the top 3 unresolved issues in my Sentry project" — verifies Sentry MCP
• "Who is on call right now according to PagerDuty?" — verifies PagerDuty Skill

Step 4: Add Grafana or Elastic for Open-Source Stacks

If you use Prometheus and Loki instead of Datadog, add the Grafana Skill pointing to your Grafana instance URL and service account token. For Elasticsearch-based log storage, add the Elastic Skill with a read-only API key scoped to your log indices.

Workflow: Ingest → Detect → Analyze → Alert

The AI-agent log monitoring workflow follows four phases designed to move from raw signal to resolved incident as quickly as possible.

Phase 1: Ingest

The agent queries all connected observability sources simultaneously. A single prompt like "Check system health across all services for the past 30 minutes" triggers parallel tool calls to Datadog MCP for metrics, Sentry MCP for new error events, and Elasticsearch Skill for log volume trends. The agent collects all responses before analyzing.

Phase 2: Detect

The agent compares the ingested data against known baselines or user-defined thresholds. It identifies anomalies — error rate above normal, latency percentile spike, sudden log volume drop — and ranks them by potential severity. Detection is contextual: a 10% error rate spike during a deployment window is treated differently than the same spike on an idle Sunday morning.

Phase 3: Analyze

For each detected anomaly, the agent digs deeper. It reads Sentry stack traces, correlates them with Datadog deployment markers, and searches Elasticsearch for related error messages. The output is a plain English root cause hypothesis: "The spike in 500 errors on /api/checkout began 4 minutes after the v2.3.1 deployment and correlates with a NullPointerException in PaymentService.java line 142."

Phase 4: Alert

Based on severity, the agent takes escalation action. For low-severity findings it posts a Slack summary. For high-severity incidents it creates a PagerDuty incident via PagerDuty Skill, assigns it to the on-call engineer, and attaches the root cause hypothesis as a note — giving responders context before they even open the monitoring dashboard.

Comparison Table

Use this table to match each monitoring skill to your observability stack and incident workflow.

Frequently Asked Questions