AI Agents for Accounting: Use Cases, Oversight, and How to Evaluate Them

For too many finance teams, accounting still runs on manual effort: coding transactions, reconciling accounts, chasing approvals, and pulling reports by hand.

Tipalti research shows that two-thirds of teams saw manual work grow last year.

AI agents for accounting can remove much of that work. The only catch is that almost every vendor now claims to have one, and many are just RPA with a new label.

So which accounting tasks can real AI agents actually handle, and which still need a person?

This guide covers where AI agents fit in the accounting cycle, how your team stays in control, and how to choose the right ones.

What Are AI Agents in Accounting?

An AI agentis software that can complete a defined, multi-step accounting task within the controls your organization sets, maintain a full audit trail of every action it takes, and escalate any decision that requires human judgment. You define the guardrails and the agent handles the routine steps, working through standard exceptions where it has enough context.

That’s different from an AI assistant, which responds when you ask it something—and from traditional automation and robotic process automation (RPA), which follow fixed, preset rules. An AI agent acts autonomously without waiting for a prompt.

Say an invoice lands in your inbox. An AI agent reads it, pulls the key details, and codes it to the right account. If everything checks out, it moves the invoice to the next step (i.e., routing it to the right team member or another agent).

If something looks wrong or the agent isn’t sure, it stops and sends the task to a person.

AI agents for accounting are tuned to financial documents, so they read invoices, receipts, and journal entries far more reliably than general-purpose AI models or AI chatbots can.

More companies are starting to use this form of AI. A third of enterprise software applications will include agentic AI by 2028, Gartner predicts—up from less than 1% in 2024.

How AI Agents Differ from RPA and Rule-Based Automation

In addition to AI assistants, you may already use rule-based automation and RPA. 

As these tools follow instructions you set in advance, they can work well when the financial data you give them is clean and predictable. However, many break when something doesn’t fit the rules: a new invoice layout, a missing purchase order (PO), or an exception that needs a judgment call, for instance.

AI agents handle more of those cases. They read messy inputs, adapt to what they find, and choose what to do next within the controls you set.

Here are the key differences:

These differences are what let autonomous agents take on real accounting work, where even the most routine tasks carry serious responsibility.

Where AI Agents Fit in the Accounting Workflow

AI agents are most useful for repetitive, multi-step work that spans the accounting cycle, from capturing data to closing the books.

Here are the main jobs they can take on, along with examples of how a helpful agent does them differently.

1) Capturing Transaction Data

This is where an agent pulls key details from each transaction—invoices, receipts, revenue events—and sends them to your ERP.

Most accounting work starts with data extraction. When teams handle that manually, every new transaction adds another chance for delays or errors.

On the payables side, Tipalti’s Invoice Capture Agent extracts vendor, amount, date, and line-item information from invoices in multiple languages and formats, whether they arrive as PDFs, email attachments, or via a supplier portal.

For employee spend, the Expense Receipt Scan Agent does the same with receipts, extracting the details and speeding up expense reporting.

As each item arrives, Tipalti AI flags duplicate bills and anomalies for review, so errors get caught before payment.

2) Coding and Journal Entries

For coding, an agent suggests GL codes for each line once the transaction details check out.

By hand, someone has to choose the right account for each line and key it in, which is slow and easy to get wrong. For example, 24% of finance teams said inefficient AP processes had led to errors in their team’s work, as reported in Tipalti’s Global Finance Outlook.

For payables, Tipalti’s Invoice Capture Agent uses coding history, vendor info, and invoice detail to suggest GL codes as it processes each bill. Once approved, that coded data can sync back to your ERP.

Anything the agent is unsure about is sent to a person for confirmation. You and your team retain full control over the accounting decisions that require review.

3) Matching and Approvals

Here, agents help transactions move through 2 common bottlenecks: matching and approval routing.

These steps are slow to handle by hand because every invoice must be checked against the correct documents and then sent to the appropriate person for sign-off.

Tipalti’s PO Matching Agent compares the invoice to the purchase order and the receipt, flags those that align, and routes the rest to someone for review.

The Bill Approvers Agent predicts and recommends the correct approver for each invoice, and they can sign off directly from their email without logging into the platform.

4) Reconciling Accounts

An agent can compare bank-feedactivity with synced records as transactions arrive, flagging mismatches.

When details line up, it can mark the item as reconciled. When they don’t, it can show the mismatch and route the exception for review.

Handled this way, reconciliation stays active throughout the month rather than leaving every mismatch to the close. There are fewer loose ends for the team to trace manually when the pressure is highest.

Where Tipalti fits is keeping the underlying records aligned. The ERP Sync Resolution Agent diagnoses sync issues between Tipalti and your ERP or accounting software.

The agent guides you through each fix so discrepancies don’t accumulate into bigger reconciliation problems.

5) Closing the Books

By the time close arrives, agents have already done much of the groundwork, so the month-end crunch starts from a cleaner base.

Instead of chasing unreconciled items and last-minute corrections, your team starts with a tidier set of records and a clearer list of exceptions to resolve.

Because Tipalti syncs approved payables and coding back to your ERP in real time, the records feeding your close stay current across entities, leaving less to reconcile when it matters most.

6) Financial Reporting

Once the numbers are in, agents can pull the data your team needs for analysis and period-end reporting.

Tipalti’s Reporting Agent, for example, creates custom outputs from plain-language prompts. Ask it for unpaid invoices over a set amount, and it can return the answer almost instantly.

That efficiency led SugarCRM accounting manager Sondra Brandt to say:

Those same reports also support the period-end analysis behind US GAAP or IFRS reporting, where your team has to explain how the numbers changed. 

Variance analysis compares actuals against a budget or forecast, while flux analysis shows how account balances moved from one period to the next.

7) Tax Preparation

For tax, agents can gather and validate the documentation behind filing long before deadlines arrive.

Done by hand, tax prep often means chasing down supplier forms and checking each one at year-end, when time is tightest. An agent collects and verifies those details as suppliers are onboarded, so the groundwork is already in place.

Tipalti’s Tax Form Scan Agent reads forms like W-9s and W-8s during supplier onboarding and gathers the details behind 1099 and 1042-S filing, ready when you need them.

When to Use Accounting AI Agents vs. Traditional Automation

For simpler accounting tasks, a cheaper, faster fit may be all you need: general-purpose AI or OCR (optical character recognition) technology to read data straight off a document.

Here’s a quick way to decide:

As a rule, the more judgment and moving parts a task involves, the stronger the case for an AI agent. For clean, predictable work, traditional automation can still get the job done.

The Human Oversight Model: Why Accountants Stay in Control

Agents handle the routine execution, while your team makes the judgment calls. That responsibility split is the very point of using AI agents in finance.

Agents handle repetitive volume so your accountants can focus on the higher-value work they’re increasingly expected to do, such as reviewing exceptions and interpreting results.

Three-quarters (74%) of finance teams said they’ve been asked to play a more strategic role in driving business growth, Tipalti’s Global Finance Outlook found. 

How Much Should an Agent Do on Its Own?

AI agent autonomy runs on a spectrum.

• Recommendation-only: The agent prepares the work and waits for a person to approve each step. For example, it might suggest an account code but give your team the final choice.
• Exception-based: The agent handles routine work on its own and pauses when something looks unusual. A clean invoice can move through capture, coding, and matching, while a missing PO or an unusual amount goes to a person for review.
• Touchless for low-risk tasks: The agent completes a well-defined, low-risk task from start to finish. Even then, it should still follow your controls and record what happened.

The right level is your decision, and it generally depends on the task. 

A low-risk, high-volume admin task may only need a light touch. Anything that moves money, affects the ledger or creates a compliance risk should keep a person in the loop.

An AP automation solution should let you set that level of control by workflow so each agent works within the guardrails your team needs.

Why Human Oversight Still Belongs in the Workflow

Finance teams don’t want autonomy without visibility.

Over half (55%) of respondents in Tipalti’s State of AI in Finance study rated the ability to see and review the actions AI takes as extremely important.

Stronger governance frameworks around ethics and transparency topped the list of what teams want before they’ll trust AI, too, at 52%.

The best accounting AI agents fit these requirements by:

• Keeping audit trails of what they did.
• Applying role-based permissions so people only approve what they’re cleared to.
• Holding low-confidence items for review.

These qualities let a controller trace any entry back to its source, sign off with confidence, and stand behind it when auditors ask.

Oversight is what makes AI automation practical for accounting. 

The accountant’s role shifts from doing every step by hand to managing and supervising the work: reviewing exceptions, correcting issues, and making judgment calls while routine tasks run in the background.

How to Evaluate AI Agents for Accounting

The best agents are built into the broader systems you already run and designed for control.

It’s worth being choosy because not every “AI agent” on the market is what it claims to be. 

Gartner estimates only about 130 of the thousands of vendors claiming agentic AI are the real thing, and points to widespread “agent washing”—rebranding chatbots, RPA, and assistants as agents to capitalize on demand.

Look past the demo and check for these 6 things instead:

• A complete audit trail. Every action the agent takes should be logged and traceable so you can show an auditor what happened, when, and why.
• Entity-level controls. If you run more than one entity, the agent should apply the right rules to each and give you a consolidated view across all of them.
• Role-based permissions. The agent should respect who can approve what, by department, entity, and amount, and send exceptions to the right person automatically.
• Fraud and compliance screening. Look for built-in checks that flag duplicate invoices, unusual vendor activity, and payments that need a closer look before money goes out.
• Native ERP integration. The agent should connect directly to your ERP or accounting software so records stay in sync and you don’t waste time on manual file exports.
• Built-in human review. The agent should pause on anything it’s unsure about and hold it for a person, so nothing that could be a compliance risk goes through unchecked.

These 6 criteria will tell you quickly whether an agent is ready for real accounting work.

Note: Some platforms go further with their own agent builders, which let you configure new agents according to your own rules.

How Tipalti’s AI Agents Support Accounting and AP Workflows

Tipalti’s AI agents run across accounting workflows, starting with Accounts Payable, while your team retains control over reviews and approvals.

Each one owns a step in the full AP cycle—capture, coding, matching, approvals, reporting, and ERP sync—working from the same data and the same rules.

Here’s how that design answers the criteria above:

• Every action taken by Tipalti’s AI agents is logged, and approvals, payments, and audit trails are consolidated in a single view across your entities, so a controller can see who approved what and trace each entry back to its source.
• The agents work within Tipalti’s existing role-based permissions, so people only approve what they’re cleared to. This helps to keep your financial data secure.
• Duplicate bill detection flags anomalies before payment, and tax compliance runs on a KPMG-approved engine with TIN matching.
• Records sync with your ERP in real time, keeping reconciliation and the month-end close current across entities.

Most importantly, there’s full oversight: anything an AI agent is unsure about is held for a person to review.

NEXT Insurance: A Simpler Approval Process and a Faster Close

US-based NEXT Insurance had a small finance team weighed down by manual invoice processing and vendor management. 

Documents were keyed into NetSuite by hand, and approvals meant tracking down department sign-offs over email. Someone was constantly organizing what was and wasn’t approved.

Upgrading to Tipalti Accounts Payable enabled the team to process more than 1,000 invoices a month. With the Bill Approvers Agent, it saved over 2 hours a week on approvals. 

Cutting the manual data entry sped up the monthly close and freed the finance team to focus on more strategic work as the business scaled toward 300,000+ customers.

As Byron Whitman, corporate controller at NEXT, puts it:

Next Steps: Where to Start with AI Agents in Accounting

AI agents are best suited to the repetitive, multi-step work that slows the accounting cycle: capture, coding, matching, reconciliation, and reporting.

For teams still building confidence with AI agents, a good starting point is a high-volume workflow where the agent’s output is easy to review before it affects payment or the ledger, such as invoice capture. 

Leading accounting AI platforms should explain why an agent recommended a particular coding decision, approver, or workflow so reviewers can validate the reasoning before approving financial transactions.

Keep a person on exceptions, review the results, and expand from there as your team gains trust in the process. Finance can reclaim time without giving up control of the numbers.

To see how AI agents work effectively across the AP cycle, explore Tipalti’s Finance AI capabilities.