Scenario Guide

AI Incident Response: Automated Triage & Resolution

Production incidents are high-stakes, time-pressured events where slow triage and inconsistent runbook execution directly translate into user impact and revenue loss. AI agent skills change the economics of on-call engineering: the agent responds to alerts in seconds, executes runbooks consistently, keeps stakeholders informed automatically, and generates post-mortems without requiring an engineer to spend an hour writing one after a stressful outage. This guide covers the five essential incident response agent skills and how to build a workflow that handles the mechanical parts of incident management autonomously.

Table of Contents

  1. 1. What Is AI Incident Response
  2. 2. Top 5 Incident Response Skills
  3. 3. Step-by-Step Setup
  4. 4. Alert-to-Post-Mortem Workflow
  5. 5. Comparison Table
  6. 6. FAQ (7 questions)
  7. 7. Related Resources

What Is AI Incident Response

AI incident response is the application of AI agents to the detection, triage, mitigation, and learning phases of production incident management. Using the Model Context Protocol, an AI agent can receive structured alert data from PagerDuty, query Sentry for the root error and stack trace, execute the appropriate runbook, post status updates to Slack, and generate a post-mortem document — all without requiring a human to manually coordinate between these systems during the most stressful part of the engineering workflow.

The key advantage of agent-driven incident response over traditional alerting is context aggregation. When an alert fires, a human on-call engineer must context-switch between PagerDuty, Sentry, Grafana, Slack, and the runbook wiki to understand what is happening and what to do. The agent performs this aggregation automatically: it reads all available signals, correlates them, and presents a unified incident picture within seconds of the alert firing.

Well-architected incident response workflows use agents for the deterministic parts (runbook execution, status updates, post-mortem drafting) while keeping humans in the loop for judgment calls (severity escalation decisions, rollback approval, customer communication). This hybrid approach reduces mean time to acknowledgment (MTTA) and mean time to resolution (MTTR) while preserving human oversight for decisions with significant consequences.

Top 5 Incident Response Agent Skills

These five skills form a complete incident response system, covering every phase from initial alert to completed post-mortem.

PagerDuty Skill

Low

PagerDuty

Connects your AI agent to the PagerDuty Events and REST APIs. The agent can acknowledge alerts, escalate incidents, reassign on-call engineers, update incident status, and retrieve the full event timeline — turning manual on-call triage into a structured, agent-driven process.

Best for: Alert acknowledgement, escalation management, on-call reassignment, incident timeline retrieval

mcp-pagerduty

Setup time: 5 min

Sentry MCP

Low

Sentry

Exposes Sentry error events, stack traces, release data, and issue assignments as agent-readable resources. The agent can query for the most recent error matching a symptom, retrieve the full stack trace, identify the commit that introduced the regression, and assign the issue to the responsible engineer.

Best for: Error investigation, regression identification, release correlation, issue assignment

@sentry/mcp-server

Setup time: 3 min

Slack MCP

Low

Slack / Anthropic

Reads channel history, posts formatted messages, creates incident channels, and sends direct messages through the Slack API. During incidents, the agent uses Slack MCP to broadcast status updates, loop in subject matter experts, and maintain a real-time incident log visible to all stakeholders.

Best for: Incident channel creation, stakeholder notifications, status broadcasts, expert escalation

@modelcontextprotocol/server-slack

Setup time: 5 min

Runbook Executor Skill

Medium

Community

Reads runbook documents (Markdown or Confluence pages) and executes their steps as agent actions. The agent interprets each runbook step, calls the appropriate tool (restart service, drain load balancer, flush cache), and logs the result — providing structured, auditable execution of your incident response procedures.

Best for: Automated remediation, runbook compliance, audit-trail execution, service restart sequences

mcp-runbook-executor

Setup time: 10 min

Post-Mortem Generator Skill

Low

Community

Synthesises the incident timeline from PagerDuty, Sentry, Slack, and runbook execution logs into a structured post-mortem document. Generates a five-why root cause analysis draft, identifies contributing factors, and produces an action item list with suggested owners — ready for team review within minutes of resolution.

Best for: Blameless post-mortems, root cause analysis, action item tracking, incident learning documentation

mcp-postmortem-generator

Setup time: 5 min

Step-by-Step Setup

Configure all five incident response skills with your existing monitoring and communication platform credentials.

Step 1: Configure MCP Skills

{
  "mcpServers": {
    "pagerduty": {
      "command": "npx",
      "args": ["-y", "mcp-pagerduty"],
      "env": { "PAGERDUTY_API_KEY": "$PAGERDUTY_API_KEY" }
    },
    "sentry": {
      "command": "npx",
      "args": ["-y", "@sentry/mcp-server"],
      "env": { "SENTRY_AUTH_TOKEN": "$SENTRY_AUTH_TOKEN",
                "SENTRY_ORG": "$SENTRY_ORG" }
    },
    "slack": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-slack"],
      "env": { "SLACK_BOT_TOKEN": "$SLACK_BOT_TOKEN" }
    },
    "runbook-executor": {
      "command": "npx",
      "args": ["-y", "mcp-runbook-executor"],
      "env": { "RUNBOOK_DIR": "./runbooks/" }
    },
    "postmortem-generator": {
      "command": "npx",
      "args": ["-y", "mcp-postmortem-generator"]
    }
  }
}

Step 2: Organise Your Runbooks

Place runbook Markdown files in a runbooks/ directory, named by service and alert type:

runbooks/
  api-high-latency.md
  database-connection-pool-exhaustion.md
  worker-queue-backlog.md
  memory-leak-nodejs.md

Step 3: Configure PagerDuty Webhook

Set up a PagerDuty webhook that POSTs alert payloads to your agent. The agent receives the alert, initiates triage, and follows the workflow below automatically when an incident is triggered.

Step 4: Run a Fire Drill

Use PagerDuty\u0027s synthetic alert feature to trigger a test incident and verify the full workflow: acknowledgment, Sentry lookup, runbook execution, Slack updates, and post-mortem generation.

Workflow: Alert Fired to Post-Mortem

  1. Alert fired — PagerDuty receives a monitoring alert and the agent is notified via webhook or polling.
  2. Triage — Agent acknowledges the PagerDuty incident, creates a Slack incident channel, and posts initial context to the team.
  3. Investigate — Sentry MCP retrieves the error stack trace and release correlation; agent identifies the probable cause and affected service.
  4. Mitigate — Runbook Executor reads the matching runbook and executes remediation steps, logging each action with its result.
  5. Post-mortem — Post-Mortem Generator synthesises the timeline, root cause, and action items into a structured document for team review.

Comparison Table

Skill responsibilities across the incident response lifecycle phases.

SkillIR PhasePrimary ActionHuman OverrideSetup
PagerDuty SkillAlert / TriageAcknowledge, escalateYes (escalation)5 min
Sentry MCPInvestigateStack trace, release blameRead-only3 min
Slack MCPCommunicateChannel creation, updatesYes (messaging)5 min
Runbook ExecutorMitigateStep-by-step remediationYes (approval gates)10 min
Post-Mortem GeneratorLearnTimeline + 5-why draftYes (review before publish)5 min

Frequently Asked Questions

What is AI incident response with agent skills?

AI incident response with agent skills means using an AI assistant to orchestrate the full incident lifecycle — from alert firing through triage, investigation, mitigation, and post-mortem — using specialised MCP skills that connect to PagerDuty, Sentry, Slack, and your runbook system. The agent acts as an always-available on-call engineer that never misses an alert, follows runbooks consistently, and generates post-mortems automatically, reducing mean time to resolution and improving incident learning across the team.

How does the agent decide which runbook to execute?

The agent matches the alert type and symptom description against a library of runbook documents retrieved via the Runbook Executor Skill. Matching can be based on alert title keywords, service name, or PagerDuty service ID. If multiple runbooks match, the agent presents the options and selects the most specific one, or escalates to a human on-call engineer if the match is ambiguous. Every runbook selection and execution step is logged with a timestamp for the post-mortem audit trail.

Can the agent resolve incidents fully automatically without human involvement?

For well-understood, repetitive incidents with clear runbook procedures — such as restarting a crashed service, flushing an overloaded cache, or rotating a saturated connection pool — yes, the agent can execute the full resolution sequence automatically. For novel incidents or those requiring judgment calls (such as deciding whether to roll back a release), the agent escalates to a human engineer via PagerDuty and Slack while continuing to gather diagnostic information. Most teams configure a "confidence threshold" above which the agent acts autonomously and below which it pages a human.

How does the Sentry MCP identify which commit caused a regression?

Sentry tracks release versions and associates error events with the release in which they first appeared. The Sentry MCP can query for "first seen" events after a specific release tag, retrieve the release's associated commits from the connected GitHub or GitLab integration, and surface the most likely culprit commit based on which files appear in the error's stack trace. The agent then uses GitHub MCP to retrieve the specific diff and provides the engineer with a focused view of the code change that introduced the regression.

How does the agent communicate with stakeholders during an incident?

The Slack MCP creates a dedicated incident channel (e.g., #inc-2026-04-09-api-latency) at the start of the incident and posts structured status updates at configurable intervals. Updates follow a standard template: current severity, affected systems, current hypothesis, actions taken, and estimated time to resolution. The agent also sends direct messages to on-call engineers when their expertise is needed and posts a final resolution message with a link to the post-mortem draft when the incident is closed.

What does the Post-Mortem Generator Skill produce?

The skill generates a blameless post-mortem document following the industry-standard structure: incident summary, timeline of events (pulled from PagerDuty timestamps, Sentry event times, Slack messages, and runbook execution logs), root cause analysis (five-why drill-down), contributing factors, impact assessment, and action items with suggested owners and due dates. The output is a Markdown file that can be published to Confluence, Notion, or a GitHub repository for team review and historical record.

How do I test the incident response workflow without triggering a real incident?

PagerDuty supports synthetic test alerts that fire through the real alert pipeline without paging on-call engineers. Use these to run "fire drill" exercises: send a test alert, observe how the agent triages and executes the runbook, review the Slack channel updates, and check the post-mortem draft generated at the end. Run drills monthly for your most critical service tiers to validate that runbooks are current and that the agent's resolution path is correct before a real incident occurs.