AI agents for exception handling
Straight answer: your systems handle the happy path. Everything that deviates from it gets handled by a person on the phone, and that small percentage of cases consumes most of your coordination capacity. Exceptions are where operations actually spend their money.
Last updated:
What this actually looks like today
The shipment doesn’t arrive. The container misses its slot. The delivery is short. The machine stops mid-run.
Somebody notices, eventually. Then they start assembling: what was supposed to happen, what actually happened, who’s affected downstream, what the contract says, who needs telling, what the options are. That assembly work is thirty to ninety minutes per exception and it’s almost entirely lookup.
What the agent does
- Trigger. A deviation from expected state — a missed milestone, a variance, a failed check, a threshold breach.
- Reasoning. Assembles the full context: the commitment, the actual, the downstream dependencies, the contractual position, the history of similar exceptions and how they were resolved.
- Action. Resolves autonomously where the pattern is known and the authority is delegated — rebooking, renotifying, reissuing. Otherwise it presents the assembled picture with ranked options.
- Escalation. Novel exceptions and anything with contractual or financial exposure go to a human with the work already done.
Traditional vs agent-augmented
| Stage | Today | With an agent |
|---|---|---|
| Detection | Someone notices, or the customer calls | Immediate, on deviation |
| Context assembly | 30–90 minutes of lookup | Seconds |
| Downstream impact | Often missed until it bites | Mapped automatically |
| Notification | Late and partial | Immediate and complete |
| Resolution record | Inconsistent | Structured, feeding pattern learning |
Where we’ve built this
- Freight & Logistics — detention, short deliveries, missed appointments
- Ports & Terminals — gate refusals, slot misses, equipment unavailability
- Manufacturing — quality NCRs, supplier shortfalls, schedule breaks
- Waste & Recycling — route exceptions, contamination rejections, access failures
Systems we integrate with
- TMS/WMS: McLeod, Alvys, Tai, Turvo, Rose Rocket, Descartes
- ERP/MES: SAP, Dynamics, Pronto, Epicor
- Terminal: TOS platforms, gate and yard systems
- Comms: email, EDI, SMS, portal messaging
How we de-risk it
- Authority is explicit and narrow at first. The agent resolves only exception types you’ve delegated, and everything else escalates. Most clients begin with assembly-and-escalate, no autonomous resolution at all — and that alone captures most of the time saving.
- Contractual exposure always escalates. Anything touching liability, penalty or credit goes to a human regardless of confidence.
- Every resolution is logged with reasoning, which builds the pattern library that makes later autonomy defensible.
Frequently asked questions
Isn’t the whole point of exceptions that they’re unpredictable?
Most aren’t. Most are the same twenty scenarios recurring, handled from scratch each time because nobody wrote them down. The genuinely novel ones escalate — and those are the ones your people should be spending time on.
What if it resolves something it shouldn’t have?
It can only act within explicitly delegated authority, and there’s a rollback path. Start with escalate-only and expand as trust builds.
How is this different from workflow automation we already have?
Rules-based automation handles anticipated exceptions. Anything unanticipated falls out to a human. An agent reasons about cases the rules didn’t foresee.
Can it learn our resolution patterns?
It learns from your logged resolutions, but the pattern library is inspectable and editable rather than a black box.
Work out what this is costing you
The calculator takes ten inputs and shows the payback band. It won’t tell you whether your systems permit the integration — that’s what the audit tests.