How do you handle peak volumes with AI agents?

Plan for the parts that do not scale. The agent can take on more in a peak, but the people who approve its held items, the systems it writes to and the model provider's capacity all have limits. Forecast the peak, check each bottleneck, agree measures in advance, and keep the agent's bounds unchanged unless a dated exception is approved.

What breaks first in a peak?

Rarely the agent. More often one of these:

The first row is the usual surprise. If the agent holds a steady share of its work for approval, doubling the work doubles the held queue. People who approve carefully at normal volume start approving quickly at peak, which is exactly when errors are most likely.

What are the steps, and who does each?

1. Agent operations lead: forecast the peak. Month-end, a filing deadline, a product launch, a seasonal event. Evidence: the forecast with expected volumes per capability.
2. Agent operations lead and system owners: check each bottleneck. Approver capacity, downstream limits, provider quota. Evidence: a capacity check per bottleneck.
3. Process owner and risk: agree peak measures in advance. Which non-urgent work can be deferred, which extra approvers are named, and whether any temporary exception to limits is justified. Evidence: the peak plan, approved.
4. Platform team: test at volume. Run the agent at expected peak load in a test environment or on replayed cases. Evidence: load test results.
5. Team lead: run the peak. Watch held queue ageing, error rates and downstream rejections daily or hourly. Evidence: the peak monitoring log.
6. Process owner: review afterwards. What held up, what did not, what to change. Evidence: the review note.

Should you loosen the agent's limits during a peak?

Rarely, and never on the day. Raising limits to clear a queue means accepting more risk at the moment errors are hardest to catch. If the case for it is good, such as letting the agent settle a specific low-value claim type without approval during a weather event, approve it in advance, scope it narrowly, date it, and record it. When the date passes, the limit returns on its own.

What does this look like in practice?

Take a home insurer after a severe storm. Claims arrive at many times the usual rate. The agent registers claims, gathers photos and documents, and drafts settlements; handlers approve settlements above a set amount.

The insurer's storm plan, agreed months earlier, kicks in. Ten handlers from other teams are named as additional approvers and given a short briefing. Non-urgent work, such as routine policy amendments, is deferred for two weeks. A pre-approved, dated exception lets the agent settle fence and gate damage claims up to a low amount without approval, because they are frequent, simple and low-value. The model provider quota was raised in advance.

During the peak, the team lead watches held-queue ageing. On day three a downstream payments system starts rejecting writes because of a batch limit; the agent queues payments and the system owner raises the limit. When the exception's date passes, the agent's settlement limit returns to normal without anyone needing to remember.

How is a peak different from an outage?

An outage means the agent is not working and people take over; see how to handle an AI agent outage. In a peak, the agent is working and the pressure is on everything around it. A peak can cause an outage if provider or system limits are hit, so the outage runbook should be ready. If errors start appearing under load, treat them as an incident; see how to handle an AI agent incident.

Test peak conditions before go-live as part of deploying an AI agent: train, shadow, certify, widen.