GAPro
Tag-based URL targeting with schedule and CI trigger support
Evidence source: Workflow scheduler and trigger APIs
Workflows
Automate release and operational scans with policy-driven workflows, clear lifecycle states and dedupe-safe execution.
What this solves
Eliminates manual scan orchestration and gives teams reliable, repeatable execution across scheduled and CI-triggered paths.
Who is this for
- •QA leads orchestrating recurring scan operations
- •Engineering teams integrating checks into CI pipelines
- •Enterprise admins controlling queue policy and execution safety
Prerequisites
- •URL tags and run scope taxonomy defined
- •Execution windows and trigger policy agreed
- •Queue ownership and fallback procedures documented
Step-by-step
1. Define workflow target sets
Map URL tags to release-critical paths and recurring quality checks.
2. Attach schedule and CI triggers
Use cron and webhook trigger modes according to release process and risk profile.
3. Monitor batch lifecycle
Track running, completed, partial failure and cancelled outcomes with explicit owner actions.
4. Harden execution policy
Enable dedupe and queue safeguards to prevent burst overload and duplicate work.
Operational outputs
- •Batch run lifecycle timeline with status details
- •Queue-aware execution metrics and dedupe outcomes
- •Trigger audit trail for scheduled and CI-driven runs
Plan availability
- •Workflow orchestration is available in Pro and Enterprise
- •Enterprise adds stronger policy controls for scale and burst handling
- •Quota and concurrency envelopes are governed by plan tier
Related capabilities
GAPro
Batch run lifecycle states: running, completed, partial_failure, cancelled
Evidence source: Batch run service state model
GAEnterprise
Scan dedupe and queue policy controls to protect worker capacity
Evidence source: Queue policy and dedupe runtime controls
Limits and guardrails
- •Do not over-subscribe concurrent heavy runs without queue policy tuning
- •Keep trigger contracts idempotent to avoid duplicate executions
- •Use partial-failure handling rules before enabling broad automation
Expected outcome
- •Scan operations become predictable across release cycles
- •Operational overhead drops through automation and dedupe
- •Incident response accelerates with clear lifecycle visibility
Troubleshooting paths
- •If batch runs stall, inspect queue lag and worker health
- •If partial failures repeat, isolate affected target tags and retry policy
- •If CI trigger misses occur, validate webhook auth and payload contract
Certainty scorecard
workflowsSample size: 0Organizations: 0insufficient_data
Not enough evidence yet to show a reliable certainty score.
Proof
Workflow Orchestration: Example batch run state
{
"batch_run_id": "67ef...",
"status": "partial_failure",
"completed_urls": 93,
"failed_urls": 7,
"total_urls": 100,
"progress_percentage": 100
}Escalation
Need workflow hardening for enterprise scale?
Request support for trigger architecture, queue separation and operational safety controls.