Back-Office Automation Agents
Back-office work is the clearest case for an agent and the easiest to get wrong. Clear, because reading a document and putting its contents into a system is genuinely repetitive, high-volume and expensive in hours. Easy to get wrong, because the exceptions are where the money is: a mis-keyed invoice is worse than an unkeyed one, and an agent that processes 95% of documents confidently and the last 5% confidently as well is not an improvement. So the design question here is never extraction accuracy in the abstract. It is what happens to the document the agent is not sure about.
What it takes over
- Document intake and extractioninvoices, purchase orders, forms, statements, delivery notes — read from email, upload or a shared folder, with the fields your system needs pulled out and normalised.
- Keying into the system of recordwriting to the accounting package, ERP or database, matching against existing records rather than creating duplicates, and stopping when the match is ambiguous.
- Reconciliationcomparing two sources that should agree — invoice against purchase order, statement against ledger — and producing a list of the differences rather than a claim that everything matched.
- Exception queuesthe actual deliverable. Anything below the confidence threshold, anything unmatched and anything contradictory goes to a person, with the document, the extraction and the reason it stopped.
What stays with your team
- The agent does not approve payment, release funds or sign anything off. It prepares; your approval process is untouched.
- A low-confidence extraction is escalated, never submitted. The confidence threshold is a setting you own, and it starts conservative.
- Scanned documents of genuinely poor quality are a data problem before they are an automation problem, and we will say so rather than quietly accepting a worse error rate.
- Where “handling invoices” turns out to be four different processes depending on the supplier, each one is scoped separately. That discovery is the single most common cause of an overrun on this kind of build.
What it connects to
- The inbox, upload form or shared drive documents arrive in
- Accounting, ERP or the internal database that is the system of record
- Spreadsheets, where they are still the system of record
- Whatever your approval workflow already runs in
What changes once it’s running
- Documents are read and keyed without a person retyping them
- A real exception queue, so attention goes to the cases that need judgement
- Reconciliation differences are surfaced as a list rather than discovered later
- The work continues overnight, so a backlog does not accumulate against office hours
Questions about back-office automation agents
What accuracy can we expect?
Any specific number quoted before seeing your documents would be a guess, and the honest version of this answer is that accuracy is measured on your own sample during the build rather than promised beforehand. What matters more is the shape of the errors: the system is configured so that uncertainty produces an escalation rather than a confident mistake. A known exception queue is manageable. Silent errors in a ledger are not.
Our invoices come in twenty different layouts. Does that break it?
No — layout variation is normal and is handled by reading the document rather than by matching a fixed template, which is the main practical difference between this and older OCR-and-rules tooling. What does need care is variation in the process rather than the layout: if different suppliers are treated differently for real business reasons, those are separate workflows and are scoped as such.
Does it need access to our accounting system?
To write records, yes — scoped to what it needs, with its own credentials, and audited. Where you would rather it did not write directly, the alternative is a prepared batch your team imports or approves, which keeps the agent on the reading side of the boundary. Both are legitimate; the choice is usually about your controls rather than about the technology.
How do we know it is still working correctly in six months?
Because it is monitored and reported on, and because the exception rate is the metric that tells you. A drift in extraction quality, or a supplier changing their document format, shows up as a rising exception rate before it shows up as a wrong number in the ledger — which is the entire argument for watching the queue rather than only the output.