Map exception classes
Identify the exception patterns that stop workflow automation from resolving work safely.
Deliverables
- Exception taxonomy
- Trigger definitions
- Evidence requirements
Příručky
Workflow exception control
A playbook for classifying, routing, measuring, and reducing exceptions in AI-assisted workflows.
Target outcomes
Conditions that should be true before expanding this workflow in production.
Příručky
Exception classes have named owners, fallback routes, and evidence requirements.
Příručky
Containment metrics distinguish safe automation from hidden rework.
Příručky
Recurring exceptions become backlog items with operational and technical owners.
Execution stages
Each stage produces operational artifacts that client teams can review and run.
Assign routing policy
Map each exception class to owner queues, fallback paths, service targets, and user-facing messages.
Deliverables
- Owner routing matrix
- Fallback policy
- Service target map
Measure containment
Instrument clean containment, assisted containment, reopens, manual rescue, and downstream rework.
Deliverables
- Containment metric set
- Workflow cohort dashboard
- Quality review sample
Reduce recurrence
Review exception patterns on a fixed cadence and convert recurring failure modes into remediation backlog.
Deliverables
- Exception review cadence
- Remediation backlog
- Automation expansion decision log
Recommended resources
Learn references that support this playbook in delivery.
Reporty
Linked operations report
Snapshot view used to track progress and health for this playbook.
Support workflow report
Operational view focused on support throughput, escalation pressure, and queue quality.
- Escalation rate vs resolved throughput.
- Queue delay risk by status bucket.
- Priority mix and response ownership gaps.
Related routes