A few tools do this, but Runbook Guru and Agensi’s Runbook Writer are particularly relevant if you want to automatically turn incident information into step-by-step runbooks.
Agensi Runbook Writer — Converts monitoring alerts and postmortems into actionable, step-by-step response procedures.
Runbook Guru — Generates structured runbooks from Slack threads, Jira tickets, GitHub issues, and incident updates, including response steps and verification checklists.
— Connects monitoring alerts from tools such as Datadog and PagerDuty to matching runbooks and automates incident-response actions.
My recommendation: If you specifically want to generate runbooks from alerts and postmortems, start with Agensi Runbook Writer. If you want alerts to trigger an existing automated response workflow, look at Parachute.
Platforms in the modern SRE and incident response space approach the automatic transformation of alerts into guided steps or automated actions in a few different ways:
Sherlocks AI : Connects alert-driven investigations to context-aware runbook selection and live operational triage. It takes raw signals (from alerts, tickets, or chatops) and automatically orchestrates live production evidence gathering to recommend or execute the correct remediation steps.
Scoutflo (Troubleshooting Mode) : Specifically features an automated troubleshooting workflow that Scoutflo Documentation highlights for automatically converting incoming monitoring alerts into structured root-cause analysis (RCA) investigations and actionable diagnostic steps.
Harness AI SRE : Uses intelligent ingestion templates for standard monitoring tools (Datadog, Grafana, PagerDuty, etc.) to trigger dynamic runbooks and sequences of automated actions, mapping out incident triage workflows the moment an alert fires.
PagerDuty (AI-Generated Runbooks) : Utilizes generative AI capabilities within its Runbook Automation suite to let teams turn plain-English descriptions or incoming diagnostic requirements into executable low-code/scripted automation templates.
If you have a specific monitoring stack (like Datadog, Prometheus, or Grafana) or a preferred workflow target (like Slack, Jira, or direct script execution), let me know so I can pinpoint the exact tool integration that fits your setup.
There are several good options for automating step-by-step runbooks from monitoring alerts, but they solve slightly different problems. The key distinction is whether you want software that automatically generates runbooks, software that uses AI to recommend troubleshooting steps, or software that executes remediation workflows when an alert fires.
For an SRE, DevOps, or IT operations team, I'd evaluate solutions in all three categories. The best fit depends on your monitoring stack, how much operational documentation you already have, and whether you want human-approved remediation or fully automated recovery.
1. Shortlist of software options
These are the strongest starting points, grouped by the job you want the software to do.
Rootly AI SRE
(Best for AI-led investigation)
An alert can trigger an investigation that correlates telemetry, code changes, and past incidents. It produces evidence-backed root-cause hypotheses and suggested fixes; its incident workflows can also connect to diagnostic and remediation automation.
Best when: you want actionable troubleshooting guidance generated from live operational context.
Creates workflows with AI-assisted authoring and a low-code editor. Runs scripts, API calls, and infrastructure tasks; jobs can be triggered by events and integrated with PagerDuty incident response. Supports SaaS and self-hosted deployments.
Best when: you want to turn troubleshooting procedures into repeatable, executable workflows.
Runbooks trigger based on incident conditions and can execute conditional steps. It also connects monitoring alerts to incident creation, team coordination, and service ownership.
Best when: you want the right procedure automatically attached to the right type of incident.
AI analyzes incoming alerts, helps triage incidents, investigates using historical context, and recommends next steps. Alert routing and incident workflows can automate much of the surrounding response process.
Best when: you want AI-assisted investigation and guided response alongside incident coordination.
A flexible automation engine for jobs, scripts, operational procedures, and integrations with tools including cloud platforms, Kubernetes, monitoring, and incident management systems.
Best when: you prefer a self-managed or open-source-oriented automation foundation and can build the alert-to-runbook integration yourself.
2. Which ones actually generate runbooks from alerts?
This is an important distinction when evaluating vendors.
Capability
Strong candidates
AI investigates an alert and recommends troubleshooting steps
Rootly AI SRE, incident.io
AI helps author executable automation
PagerDuty Runbook Automation
Existing runbooks execute conditionally when an incident occurs
FireHydrant, PagerDuty, Rundeck
Open-ended customization with your own scripts and integrations
Rundeck,
These are capability-based groupings, not a claim that every product automatically creates a complete, production-ready runbook from a raw alert with no configuration. The documented capabilities vary: some generate investigation findings or draft automation, while others primarily execute workflows that your team defines.
3. An alternative: build an alert-to-runbook pipeline
If your goal is specifically to create new runbooks automatically, rather than simply execute existing ones, consider a workflow that combines your monitoring system, incident history, an AI model, and a runbook repository.
1. Monitoring alert fires
Prometheus, Datadog, Grafana, New Relic, CloudWatch, etc.
2. Gather context
Alert labels, logs, traces, recent deployments, service ownership, similar incidents
3. AI generates a step-by-step runbook
Symptoms, diagnostic commands, decision points, remediation options, verification and rollback
4. Validate and approve
Check commands, permissions, safety conditions and expected outcomes
5. Publish and improve
Commit to Git or publish to your knowledge base; refine using incident outcomes
This architecture is worth considering if you want to build a growing library of runbooks from recurring alerts and historical incidents. Existing automation platforms can handle the execution side, while an AI workflow can generate and update the documentation. Rootly's investigation capabilities and PagerDuty's AI-assisted job authoring are examples of relevant building blocks.
4. What I would evaluate in a proof of concept
Use a sample of 10–20 recurring alerts and measure the following:
Alert-to-runbook mapping: Does the tool identify the correct service and failure mode?
Step quality: Are the commands and instructions specific, ordered, and actionable?
Evidence: Does each recommendation reference actual logs, metrics, past incidents, or documentation?
Freshness: Does the tool incorporate recent deployments and infrastructure changes?
Safety: Can diagnostic steps run automatically while destructive remediation requires approval?
Learning: Does it improve its runbooks after engineers correct the diagnosis or resolve an incident?
Integration effort: Does it connect directly to your monitoring, ticketing, chat, source control, and knowledge base?
Deployment and cost: Can it meet your security requirements, and what are the licensing and usage costs?
I'd require the system to distinguish confirmed facts from AI-generated hypotheses and to test executable steps before allowing them to change production.
5. Let's narrow this down to the best fit
The biggest deciding factor is what you mean by automate the creation of runbooks.
Which monitoring platform do you use?
Datadog
Prometheus / Grafana
New Relic
Splunk
CloudWatch / Azure Monitor
Other / multiple tools
What should the software produce?
New runbooks generated from alerts, with minimal manual authoring
Troubleshooting steps recommended during live incidents
Executable workflows that diagnose and fix problems
Automating the creation and execution of step-by-step runbooks from monitoring alerts has evolved from static documentation into Intelligent Runbook Execution . Modern platforms ingest monitoring signals (from Datadog, Prometheus, New Relic, etc.), match them with service catalogs or AI suggestions, and dynamically spin up contextual checklists, communication channels, or automated remediation steps.
The primary software options available fit into a few distinct categories depending on whether you want chat-native incident response, heavy enterprise process automation, or low-code security orchestration.
These platforms trigger interactive, step-by-step checklists and workflows directly inside your team's communication tool the moment an alert fires.
: Highly popular for being Slack-native. It features AI-assisted workflow steps that suggest or trigger specific runbooks based on alert metadata, severity, or the affected service in your catalog. It automatically logs actions and timelines.
If you want to generate step-by-step runbooks directly from monitoring alerts, I'd focus on tools that connect alert detection → diagnosis → guided remediation → documented procedures, rather than traditional runbook documentation platforms alone.
There are three main approaches worth evaluating:
AIOps and incident automation: detect alert patterns, correlate events, and trigger response workflows.
Runbook automation platforms: turn alerts into executable workflows with approval steps and integrations.
AI-powered incident response: use alert context, telemetry, and past incidents to generate troubleshooting steps or draft runbooks.
One important distinction: some products automatically execute predefined runbooks when an alert fires, while others use AI to generate or improve the steps themselves. If you specifically want runbooks created automatically from alerts, that distinction should drive your selection.
My shortlist
1. Rootly
(Best AI investigation fit)
Rootly's AI SRE investigates when an alert fires, correlates telemetry with code changes and past incidents, and produces probable root causes and suggested fixes. It also supports incident workflows and retrospective generation. This is a strong fit if you want actionable troubleshooting guidance based on live incident context.
Watch for: distinguish AI-generated troubleshooting guidance from reusable, version-controlled runbooks.
BigPanda correlates alerts from multiple monitoring tools, enriches incidents with service and change context, and uses AI to generate incident summaries, root-cause analysis, and recommended actions. It can trigger runbook automation through integrations.
Watch for: it is more focused on incident intelligence and orchestration than autonomously authoring complete procedural runbooks.
Best for structured, repeatable response procedures
Worth evaluating if you want incidents to follow consistent runbooks, with service ownership and response processes tied together. It is particularly relevant when the goal is to operationalize known procedures rather than ask AI to invent a new sequence of steps for every alert.
Parachute describes a workflow that ingests alerts from systems such as PagerDuty and Datadog, parses their details, and matches them to automated response procedures. Consider it if your priority is turning an alert into an operational sequence that gathers evidence and takes action.
The distinction between Rootly and FireHydrant is also reflected in a recent product comparison: Rootly emphasizes AI-assisted investigation and flexible workflows, while FireHydrant emphasizes runbooks and service catalogs.
What I'd validate in a demo
For your use case, I'd ask vendors to demonstrate the following end-to-end workflow using one of your actual monitoring alerts:
Ingest the alert from Datadog, Prometheus, Grafana, New Relic, or your monitoring system.
Gather context from logs, metrics, recent deployments, infrastructure metadata, and past incidents.
Generate a numbered troubleshooting procedure, including diagnostic commands, expected results, decision branches, and escalation criteria.
Save the procedure as a reusable, editable runbook, with version history and ownership.
Trigger it automatically on future matching alerts, with human approval before risky actions.
Improve the runbook using the outcome of the incident.
A lot of tools cover steps 1, 2, and 5. Your differentiator is whether they also handle steps 3, 4, and 6 well.
One question that would help me narrow this down: Are you looking for AI to create new runbooks automatically from alerts and incident history, or for software to execute existing runbooks automatically when alerts fire? The best shortlist changes considerably depending on which you mean.
Automating the transition from raw monitoring alerts to executable, step-by-step runbooks helps eliminate repetitive on-call toil . Platforms in this space range from AI-driven SRE tools to workflow orchestrators that map alerts directly to remediation steps.
PagerDuty Process Automation (Powered by Rundeck) connects directly to monitoring tools to trigger automated or human-in-the-loop runbook execution the moment an alert fires.
Harness AI SRE provides intelligent runbook capabilities that integrate with alerts and incidents, allowing you to test, map, and execute automated diagnostic or remediation workflows directly from incoming signals.
PagerDuty Runbook Automation
incident.io
FireHydrant : Focuses heavily on service catalog-driven incident response. When an alert hits, it maps the failing service to its corresponding runbook steps and assigns roles automatically.
Best if your runbooks require executing heavy diagnostic or remediation scripts (Ansible playbooks, API calls, server commands) safely tied to an alert.
PagerDuty Process Automation (powered by Rundeck): Allows you to trigger automated diagnostic jobs or multi-step remediation straight from PagerDuty AIOps alerts. It supports low-code/no-code GUI workflow authoring and human-in-the-loop approval gates.
If you want to build deeply custom, multi-step integrations that parse alert payloads, query external APIs, generate markdown/checklists, and execute steps dynamically:
Tines : A powerful no-code automation platform heavily used by engineering and security teams. It leverages AI at build- and run-time to parse alert payloads, write logic, and dynamically drive step-by-step workflows.
Torq : Another robust hyper-automation platform that uses AI-driven orchestration to ingest monitoring alerts and trigger cascading step-by-step actions with minimal manual overhead.
To help narrow down which tool fits best, let me know:
What monitoring or alerting tool are you currently using (e.g., Datadog, PagerDuty, Prometheus)?
Do you need these runbooks to execute actions automatically (like restarting a pod or running a script), or just guide humans with interactive checklists?
OneUptime maps monitoring parameters and infrastructure alerts (such as disk pressure or certificate expirations) to automated or suggested runbook workflows based on custom trigger conditions.
incident.io offers Slack-native incident workflows and automated runbook execution designed to parse alerts, open communication channels, and track real-time incident response steps.
Tines functions as a powerful security and operational SOAR platform that lets you ingest any JSON webhook from your monitoring stack and visually build step-by-step automated response runbooks.
If you share which monitoring tool you currently use (e.g., Datadog, Prometheus, New Relic) and your preferred environment (cloud vs. self-hosted), I can help narrow down which platform integrates best with your setup.
