Putting AI agents to work in back-office operations without losing control

The useful version of AI in operations is unglamorous. It reads the supplier email, checks it against the purchase order, flags the discrepancy and drafts the reply. It triages support tickets by looking up the order. It reconciles the carrier invoice against shipments. None of this is a chatbot, and none of it should be fully autonomous on day one.

Where agents earn their keep

The best candidates share four traits: the work is repetitive, the inputs are messy but the rules are knowable, a wrong answer is recoverable, and a human can verify the output quickly. Document processing, exception detection, data entry between systems and first-pass triage all qualify. Approving a refund over a threshold or changing a price does not, at least not without a person in the loop.

The controls that matter

Give the agent read access to approved sources and narrow write access to specific actions. Set a confidence threshold below which the task routes to a person. Log every input, output and action so a mistake can be traced and the system improved. Evaluate against a set of known cases before launch and keep evaluating after. Track cost per task, because a workflow that costs more in tokens than it saves in labor is not automation.

Run it in parallel first

For the first weeks, the agent does the work and a person does it too. You compare, measure agreement, fix the gaps and only then let the agent act alone within its threshold. This is slower than flipping a switch and far faster than recovering from a quiet month of wrong invoices.

The mistakes we see

Starting with the technology instead of the workflow. Building on data nobody has cleaned. Treating a demo as a deployment. Skipping the audit trail because the pilot is 'just internal'. And assuming that because a system runs in a cloud with a compliance badge, your use of it is compliant. It is not, until someone has verified it against your actual obligations.