Exception Management
The process of identifying, prioritizing, and resolving deviations from expected workflows or outcomes. In supply chain, operations, and finance contexts, exceptions include late shipments, invoice discrepancies, failed quality checks, and SLA breaches. AI agents automate exception management by detecting anomalies in real time, prioritizing by business impact, handling routine exceptions autonomously, and escalating complex issues with full context and recommended actions.
Written by Max Zeshut
Founder at Agentmelt · Last updated Sep 9, 2026
Example
A supply chain AI agent detects that a container ship will arrive 3 days late due to port congestion. It automatically identifies the 12 affected customer orders, recalculates ETAs, sends proactive customer notifications, and reschedules warehouse receiving—all before the logistics team starts their morning.
Frequently asked questions
- What percentage of exceptions can AI handle autonomously?
- For routine, well-defined exceptions (missed pickups, standard rescheduling, ETA updates), AI agents handle 60-80% autonomously. Complex exceptions involving multi-party coordination, regulatory issues, or significant financial impact still require human judgment—but the AI provides full context and recommended actions to speed resolution.