AI agents for finance use software tools and business data to carry out a task within defined instructions and permissions. An agent might investigate an unmatched payment, prepare an accrual or follow up on an overdue invoice.
The important distinction is the authority you give it. Answering a question, preparing a proposed action and posting to the ledger have different consequences. An evaluation should show which of those the agent can do and who remains accountable.
Which finance tasks suit an agent?
Choose a task with a recognisable end state and evidence the team can review. For collections, that might be identifying overdue invoices and preparing reminders under an agreed policy. For reconciliation, it might be proposing matches and explaining unresolved differences.
The task needs boundaries. Define the relevant entities, accounts and documents, the decisions the agent may make, and the cases it must hand back. “Keep the books correct” is too broad to test.
Start with one workflow where you can compare the result with the team's current work. The accounting automation guide gives a method for choosing that first process.
What access should a finance agent have?
Grant the access needed for its assigned work. An agent preparing a report does not need payment authority. An agent drafting journals does not automatically need permission to post them.
Inspect the identity behind each action. A reviewer should be able to tell which agent ran, what triggered it, what information it used and which person approved a consequential action. Changes to the agent's instructions also need an owner and a record.
The same internal controls that govern human work still matter: authorisation, segregation of duties, review and evidence. Automation changes how a control operates; it does not establish that the control is effective.
How do you test the difficult cases?
Include missing data, conflicting documents and a source system that stops responding. Ask the agent to explain why it cannot complete the task. A clear exception is a better result than a confident answer based on incomplete information.
For an agent that reads supplier documents, test whether text inside a document can change its instructions or permissions. Document content should provide evidence for the task, rather than authority to redirect it.
Also repeat an event and interrupt a run. Finance needs to know whether the agent can resume without duplicating a journal, message or payment instruction.
What does Light provide?
Light describes its finance agents as operating on the ledger within the company's policies and permissions. Custom agents let teams define additional work around their own processes.
For a demonstration, bring a specific task and inspect the output alongside the activity history. Ask the team to show an exception and the point at which a person takes over. That provides more useful evidence than the number of agents in a catalogue.
What should a pilot prove?
Measure completion rate, corrections, exceptions and the time people spend supervising the agent. Compare like-for-like work, including the harder cases. Record any changes to the instructions during the pilot so the result is reproducible.
A successful pilot leaves the finance team with a process it understands and can govern. The AI accounting software comparison applies the same test when choosing the wider platform.
For the effect on the team, read how AI changes accounting work. Include training and review responsibilities in the rollout plan, alongside the tasks the software will perform.
