Map exception classes
Identify the exception patterns that stop workflow automation from resolving work safely.
Deliverables
- Exception taxonomy
- Trigger definitions
- Evidence requirements
Guias
A playbook for classifying, routing, measuring, and reducing exceptions in AI-assisted workflows.
Conditions that should be true before expanding this workflow in production.
Exception classes have named owners, fallback routes, and evidence requirements.
Containment metrics distinguish safe automation from hidden rework.
Recurring exceptions become backlog items with operational and technical owners.
Each stage produces operational artifacts that client teams can review and run.
Identify the exception patterns that stop workflow automation from resolving work safely.
Deliverables
Map each exception class to owner queues, fallback paths, service targets, and user-facing messages.
Deliverables
Instrument clean containment, assisted containment, reopens, manual rescue, and downstream rework.
Deliverables
Review exception patterns on a fixed cadence and convert recurring failure modes into remediation backlog.
Deliverables
Learn references that support this playbook in delivery.
Snapshot view used to track progress and health for this playbook.
Operational view focused on support throughput, escalation pressure, and queue quality.
Related routes