Automate Incident Intake and Start Response in Seconds | Harness Blog

Manual intake eats the first 10 to 30 minutes of an incident. Harness AI SRE runs it as automation and gives those minutes back to your responders.

The first minutes go to setup

When an alert fires, most teams spend 10 to 30 minutes on coordination before anyone looks at the problem. The pattern repeats on every incident:

• An alert arrives from PagerDuty, Datadog, or another monitor
• Someone opens a Jira or ServiceNow ticket
• Someone spins up a Slack channel and pulls in responders
• Someone starts a Zoom bridge and pastes the link
• Someone hunts down the runbook or the last incident's notes

None of that work changes between incidents, and the delay compounds on every high-severity page.

Runbooks turn intake into a triggered workflow

In AI SRE, a runbook is an automated workflow that runs when a trigger fires. You build it in a visual workflow editor instead of writing glue code, with more than 50 pre-built integrations for its action steps. When the trigger condition is met, the runbook executes its steps in order and records each one.
A single intake runbook can:

• Create a ticket in Jira or ServiceNow
• Open a Slack channel and invite the right responders
• Start a Zoom bridge and post the join link to the channel
• Set severity, category, and impacted-service fields on the incident
• Write every action to the incident timeline as it happens

Setup that used to take 15 minutes runs in seconds, and the timeline leaves an ordered record for the post-incident review.

What sets the trigger off

A runbook fires on any of three signals:

• An inbound webhook from a monitoring or alerting tool. On the intake side, AI SRE ships ingestion templates for more than 20 alert sources, including Datadog, PagerDuty, BigPanda, Grafana, New Relic, Sentry, and Opsgenie, plus a generic template for any tool that posts JSON.
• Manual creation of an incident or alert, or an on-demand run of the runbook.
• A change to an existing incident, such as severity, status, or impacted services.

You scope which alerts launch a runbook with conditions. A trigger can filter on fields, tags, or thresholds through a rule builder or a CEL expression, so a Sev3 CPU blip and a Sev1 outage take different paths. Inside the runbook, a conditional step branches further: when severity is critical, page the secondary on-call team; otherwise the standard logging path runs.

The difference: response sits on your change data

Most incident tools begin with no context. They register that an alert fired and little else.
Platform teams already produce the answer. You build artifacts, deploy them through pipelines, and merge pull requests, and that record is the first place to look when something breaks. AI SRE reads it directly through the Deploy Change Investigator, which connects three streams:

• Build events carrying artifact version, commit SHA, and repository
• Deploy events carrying the services, environment, and versions that shipped
• The pull requests merged into the deployed branch

Once those are linked, the platform can name the deploy that was live when the incident started and diff the pull requests between the last healthy release and the current one. An AI agent reasons over that change history alongside recent feature-flag changes and related alerts. It then surfaces the deploys, PRs, and flags most likely behind the incident, each with a confidence level. It answers the first question in any incident, what changed, from data you already generate.

Runbook actions can act on that answer, too. A step can trigger a Harness pipeline directly (for example, to roll back the suspect deployment), so the same workflow that opened the incident can start the fix.

How to set one up

1. Build the runbook. Lay out the intake steps in the visual editor and pick the integration each step uses.
2. Add triggers. Point the runbook at the webhook sources and incident events that should launch it, then add conditions so only the right alerts qualify.
3. Add actions. Define what runs on trigger: create the ticket, open the channel, start the bridge, set the fields, page the on-call. Every action lands on the timeline.

Start with intake, then keep going

Automating intake removes the fixed coordination cost from the start of every incident and gets responders to investigation in seconds. Because AI SRE sits on the build, deploy, and change data your pipelines already produce, it does more than open the channel faster. It hands the team a first read on what changed.
Human-in-the-loop stays the default. Automation clears the setup and surfaces the likely cause; people decide and drive the resolution.

See how Harness AI SRE automates incident intake or build your first runbook today.

FAQs

What is a runbook in incident response?

A runbook is an automated workflow that runs when a trigger fires. Instead of writing custom glue code, you build it in a visual workflow editor on top of pre-built integrations, and it executes a defined set of steps in order: creating tickets, opening Slack channels, starting bridges, and more, every time it's triggered.

What triggers an incident runbook to run?

A runbook fires on one of three signals: an inbound webhook from a monitoring tool like Datadog or PagerDuty, manual creation of an incident or an on-demand run, or a change to an existing incident such as a severity or status update. Conditions let you scope which alerts actually launch the runbook, so low-severity blips and major outages can follow different paths.

How does AI SRE reduce incident response time?

AI SRE automates the coordination work that normally eats the first 10 to 30 minutes of an incident (ticket creation, Slack channel setup, bridge links, and field tagging) and runs it in seconds instead. It also connects build, deploy, and pull request data through the Deploy Change Investigator, so responders get a first read on likely causes instead of starting from zero.

What is the Deploy Change Investigator?

It's the part of AI SRE that links three data streams: build events, deploy events, and merged pull requests, so the platform can identify which deploy was live when an incident started and diff the changes since the last healthy release. An AI agent reasons over that history, along with feature-flag changes and related alerts, to surface likely causes with a confidence level.

Which monitoring tools integrate with AI SRE?

AI SRE ships ingestion templates for more than 20 monitoring and alerting sources, including Datadog, PagerDuty, BigPanda, Grafana, New Relic, Sentry, and Opsgenie, plus a generic template for any tool that posts JSON.

Can a runbook trigger a rollback automatically?

A runbook step can trigger a Harness pipeline directly. For example, to roll back a suspect deployment so the same workflow that opens the incident can also kick off the fix. That said, human-in-the-loop remains the default: automation gets responders to the starting line and surfaces likely causes, but people make the call and drive resolution.

Does automating incident intake replace human responders?

No. Automation handles the repeatable coordination work (tickets, channels, bridges, timelines), so responders can start investigating immediately. Decisions and resolution still sit with people; AI SRE's job is to remove setup time, not judgment.