An exception queue is a list of cases that an automated process or AI agent could not complete on its own and has passed to people to resolve: a payment that failed to match, a document it could not read, a request outside its role. Its health shows how well the edge of automation is drawn.
Why does an exception queue matter for agents?
Every agent hits cases it cannot handle. What matters is what happens next. Without a proper queue, those cases stall silently, bounce between systems, or get forced through. With one, each exception lands with a person who has what they need to decide, and the case keeps moving.
The queue is also a source of evidence. Patterns in exceptions show where the agent's brief, sources or bounds need work, and where a process is producing cases that nobody, agent or person, can resolve cleanly.
How does an exception queue work in practice?
A good exception carries enough for a person to act without starting over:
- what the agent was trying to do and how far it got;
- why it stopped: missing data, conflicting sources, outside role scope, a bound it hit;
- the facts it gathered, with their sources;
- any customer deadline attached to the case.
Queues need owners, service levels and routing rules, like any operational queue. The practical side of working an operations exceptions queue with an AI agent covers triage, routing and how agents can help sort the queue itself.
What does an example look like?
A business bank's reconciliation agent matches incoming payments to open invoices. Most match. Some do not: partial payments, payments with no reference, payments in the wrong currency. Each unmatched item goes to the exception queue with the candidate invoices the agent considered and why none fitted. A cash management officer works the queue each afternoon. Over a month, the officer notices that a third of exceptions come from one customer who pays several invoices at once, which leads to a change in how that customer's payments are matched.
How is an exception queue different from a held queue?
They are often confused, and they need different handling.
| Exception queue | Held queue | |
|---|---|---|
| Why the case is there | The agent could not complete it | The agent completed a draft that needs approval |
| What the person does | Resolves the case, often from scratch | Approves, changes or refuses the drafted action |
| What it signals | Gaps in data, process or the agent's role | The agent working as designed within its depth |
Mixing them slows both. A reviewer expecting ready drafts loses time on unresolved cases, and an exception handler wastes effort on items that only need a yes. How to staff the held side is covered in how to staff the queue of actions AI agents hold, and the held queue daily review checklist gives a routine for it.
Which terms sit close to it?
A work queue is the general idea: any list of tasks waiting to be done. Straight-through processing is the share of cases that need no exception at all. Escalation is when an agent passes a case up because a decision is beyond it. For where queues fit in running agents, see our guide to deploying AI agents.
Where Pryme fits
Pryme separates what an agent drafted from what it may not do. Actions above its bounds are held for a named person, with the deciding bound named in the verdict. Actions outside its bounds are refused, and that verdict is filed too, so recurring refusals show where the process or the agent's brief needs work.
Questions people ask
Should an agent ever resolve its own exceptions?
Sometimes on a retry, such as when a system was briefly unavailable. Otherwise no: if the agent could resolve it, it would not be an exception. Repeated exceptions of one kind are a reason to change the agent's brief or sources through change management, not to loosen its bounds.
What is a healthy exception rate?
There is no universal figure. Track the trend and the reasons. A steady or falling rate with clear reasons is healthy; a rising rate or a growing "other" category needs investigation.