Active
AI Generated
Multi-Service Correlation
Correlate alerts before remediation
Description
Multiple correlated alerts arrive across services. The workflow correlates them, identifies root cause when possible, and routes to targeted remediation or AI-assisted triage when not.
Why it matters
- Correlated alerts across services surface root cause before running the wrong fix on a symptom.
- Targeted remediation when cause is known; AI fallback when it is not — one workflow, two strategies.
- Reduces alert storms that trigger duplicate remediation on every firing host.
AO building blocks
Automation orchestrator step types this workflow uses on the canvas. See all building blocks
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Triggers
Manual trigger
Starts a workflow when a user clicks Run in the UI or calls the workflow execution API — the simplest entry point for on-demand runs like demos, break-glass remediation, or operator-initiated changes. No extra configuration is required, and runs appear in history with the triggering user as the source.
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AI & integrations
Task agent
Adds LLM reasoning to a workflow — the step receives a natural-language prompt, can call enabled tools, and returns structured output that downstream steps reference through expressions. Use it for unstructured data (logs, tickets, alerts) to summarize, classify, triage, and recommend remediation actions; pair with an approval step for human oversight.
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Logic
Switch step
Evaluates conditions and routes execution to the first matching branch, supporting up to 100 ordered paths plus a fallback when nothing matches. Use it to branch on severity tiers, environment (dev/staging/prod), or AI agent output categories where a binary conditional isn't enough.
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AAP execution
AAP job template
Launches a single Ansible Automation Platform job template from the workflow, polling until the job completes and exposing results to downstream steps. The standard bridge between automation orchestrator reasoning and AAP execution — one playbook with resolved inventory, credentials, limits, and extra vars.
Interactive Experience
Planned scope
- Multi-alert correlation across services
- Root-cause-first remediation when identified
- Fallback to AI triage agent
- Validation and operator notification
Workflow
flowchart LR
A@{ img: "https://ansible-tmm.github.io/aap-orchestrator-demos/assets/icons/manual-trigger.png", label: "Alert Payload", pos: "b", h: 96, constraint: "on" } --> B@{ img: "https://ansible-tmm.github.io/aap-orchestrator-demos/assets/icons/launch-aap-job-template.png", label: "Correlate Alerts", pos: "b", h: 96, constraint: "on" }
B --> C@{ img: "https://ansible-tmm.github.io/aap-orchestrator-demos/assets/icons/switch-node.png", label: "Root Cause?", pos: "b", h: 96, constraint: "on" }
C -->|Yes| D@{ img: "https://ansible-tmm.github.io/aap-orchestrator-demos/assets/icons/launch-aap-job-template.png", label: "Targeted Remediation", pos: "b", h: 96, constraint: "on" }
C -->|No| E@{ img: "https://ansible-tmm.github.io/aap-orchestrator-demos/assets/icons/task-agent.png", label: "AI Triage Agent", pos: "b", h: 96, constraint: "on" }
D --> F@{ img: "https://ansible-tmm.github.io/aap-orchestrator-demos/assets/icons/launch-aap-job-template.png", label: "Validate Recovery", pos: "b", h: 96, constraint: "on" }
E --> F
F --> G@{ img: "https://ansible-tmm.github.io/aap-orchestrator-demos/assets/icons/launch-aap-job-template.png", label: "Notify Operators", pos: "b", h: 96, constraint: "on" }
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class A,B,C,D,E,F,G noBorder
Playbooks
| Playbook | What it does | Runs on |
|---|---|---|
correlate_alerts.yml
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Analyzes alert payloads for host and service correlation patterns, publishes correlation_result artifact. | localhost |
targeted_remediation.yml
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Restarts affected services for known root cause with service-specific recovery. | RHEL node |
validate_recovery.yml
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Validates all correlated services have recovered with retry loop. | RHEL node |
notify_operators.yml
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Posts correlation analysis and remediation results to Mattermost. | localhost (Mattermost API) |
AI Generated
This demo's playbooks and workflow were generated by AI to illustrate the pattern. They have not been fully tested in a live environment. Review and adapt before using in production.