Incident Response

Incident Response

Correlate incidents with deployments, assess blast radius, and generate postmortem documents using Harness MCP.

Instructions

Step 1: Establish Scope

Confirm the affected service, environment, and incident details.

Call MCP tool: harness_list
Parameters:
  resource_type: "service"
  org_id: ""
  project_id: ""

Step 2: Identify the Incident Response Task

Determine which workflow the user needs:

1. Deployment-to-Incident Correlation -- Determine if a recent deployment caused the incident
2. Blast Radius Assessment -- Map affected services and downstream impact
3. Postmortem Generation -- Create a structured postmortem document

Step 3: Correlate Deployment to Incident

Gather from the user:

• Affected service name and environment
• Alert or incident name and start time
• Observed symptoms (error rate spike, latency, outage)

Pull recent deployments:

Call MCP tool: harness_list
Parameters:
  resource_type: "execution"
  org_id: ""
  project_id: ""
  status: "Success"

For each recent deployment, check:

• Timing: Was the deployment within the incident correlation window (e.g., 2 hours before)?
• Service match: Does the deployed service match or depend on the affected service?
• Change content: What changed in the deployment (config, code, infrastructure)?

Build a deployment timeline:

1. List all deployments to the affected environment in the last N hours
2. Mark the incident start time on the timeline
3. Identify the most likely causal deployment (closest before incident start)
4. Check if a rollback was performed and whether it resolved the issue

Present findings with confidence level: HIGH (deployment matches timing + service), MEDIUM (timing matches but different service), LOW (no deployment correlation found).

Step 4: Assess Blast Radius

Gather from the user:

• Failing service and failure type (outage, elevated error rate, high latency)
• Current error rate or severity
• Environment

Map the impact:

Call MCP tool: harness_status
Parameters:
  org_id: ""
  project_id: ""

Assess:

• Direct impact: The failing service's error rate, latency, and availability
• Upstream callers: Services that call the failing service -- are they degrading?
• Downstream dependencies: Services the failing service depends on -- are they healthy?
• User impact: Estimate affected users based on traffic volume
• Data integrity: Any risk of data corruption or inconsistency?

Classify severity:

• Critical: User-facing outage, data loss risk, or multiple services affected
• Major: Degraded performance affecting users, single service impacted
• Minor: Internal service degraded, no user-facing impact

Recommend immediate actions based on blast radius:

• If deployment-correlated: recommend rollback with expected resolution time
• If infrastructure-related: recommend failover or scaling
• If dependency-related: recommend circuit breaker activation or graceful degradation

Step 5: Generate Postmortem

Gather from the user:

• Service name and incident summary
• Incident duration and environment
• Resolution steps taken

Structure the postmortem:

1. Executive Summary -- What happened, customer impact, duration (2-3 sentences)

2. Timeline -- Build from Harness pipeline events and alert timestamps:

• When was the issue first detected?
• When did the on-call team engage?
• What deployment or change triggered the regression?
• When was mitigation applied and service restored?

Pull timeline data:

Call MCP tool: harness_list
Parameters:
  resource_type: "execution"
  org_id: ""
  project_id: ""

3. Root Cause Analysis -- Which deployment or change triggered the incident and why

4. Impact Assessment -- Affected services, environments, and approximate user impact

5. Action Items -- Categorized as:

• Immediate fixes (address the root cause)
• Process improvements (prevent recurrence)
• Monitoring improvements (detect faster)

6. Lessons Learned -- What went well, what didn't, and what was lucky

Examples

• "Our payment service is down -- was it a deployment?" -- Correlate incident with recent deployments and provide confidence level
• "What is the blast radius of the checkout outage?" -- Map upstream/downstream services and estimate user impact
• "Generate a postmortem for yesterday's auth-service incident" -- Create structured postmortem with timeline, RCA, and action items
• "A Sev-1 just fired -- which deployment caused it?" -- Pull recent deployments and correlate with alert timing

Performance Notes

• Deployment correlation is most accurate within a 2-hour window -- beyond that, other factors become more likely.
• Blast radius assessment requires an up-to-date service dependency map -- stale maps miss connections.
• Postmortems should be generated within 48 hours while the incident is fresh in the team's memory.
• Always include what went well in the postmortem -- blameless culture requires acknowledging good responses.

Troubleshooting

No Deployment Found in Correlation Window

• Expand the search window to 4-6 hours -- some failures have delayed onset
• Check for infrastructure changes (not just code deployments)
• Look for config changes, feature flag toggles, or certificate expirations

Blast Radius Assessment Missing Services

• The service dependency map may be incomplete -- check for undocumented dependencies
• Look for shared infrastructure (databases, message queues) that multiple services use
• Check for external dependencies (third-party APIs, DNS, CDN)

Postmortem Missing Timeline Events

• Pull from multiple sources: pipeline executions, alert history, and chat transcripts
• Check if automated rollbacks occurred that may not be in the deployment history
• Include infrastructure events (auto-scaling, node failures) alongside deployment events

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: harness
• Source: harness/harness-skills
• License: Apache-2.0
• Homepage: https://developer.harness.io/docs/platform/harness-ai/harness-skills/

Install and usage instructions live in the source repository linked above.