Right now, almost every software vendor in our industry is claiming AI leadership. Look closely and most of what’s sold are products that have been built because it’s become easier to build them, not because they address a tangible opportunity. So it’s worth being precise about what agentic AI actually is, and why accounting is one of the places it could matter most.
An agent acts on your behalf
The clue is in the name. AI assistants and co-pilots surface information and suggest your next step. They can tell you what to do but they can’t do it for you.
Agentic AI crosses that line. It takes a task you’ve set, gathers what it needs from the systems it’s connected to, does the work, and hands back a summary so you can check it. The human stays in control at the review stage.
And a good agentic workflow is more than generative AI on its own. It combines deterministic steps, pulling data through an API and applying structured rules, with a generative layer that interprets, drafts and explains. That combination is what makes it robust enough for professional work.
What this looks like in practice
Take the fixed asset register. Today someone works through the year’s additions and disposals in the ledger, decides which items are capital, sets each one up with a category and a useful life, then calculates and checks the depreciation.
An agent can do most of that, then explain its reasoning so the reviewer can follow the logic and sign off.
The data entry shrinks, the risk of manual error drops and the accountant’s job shifts from processing to reviewing, which is where their expertise earns its value.
Why accounting is unusually well suited to this
Three things stand out.
First, much accounting work has a right answer to check against. Reconciling payroll, recalculating loan interest, comparing VAT-return turnover with the accounts: the ground truth already exists, so the agent’s output can be verified rather than taken on trust. That’s far safer than fields where there’s no reliable way to tell if the AI got it right.
Second, much of the work is routine and repeatable, following consistent patterns across thousands of businesses even when the numbers differ. That lets you design agents with reliable, well-defined flows, combining the generative layer with deterministic logic rather than the model’s judgement alone.
Third, accounting work has to be documented and defensible. In a compliance context, the value of an agent is in how it reaches the answer: it completes the working papers, documents its reasoning and captures how source data connects through to the accounts and tax return. The deliverable is the whole audit trail from raw data to conclusion, and a good working paper platform is built to hold that trail end to end.
Where the agent stops
Here’s the part the excitable takes tend to skip. Accounting work splits in two. There’s work with a clear right answer, where the agent finds or reproduces it. And there’s work where the accountant creates the answer through professional judgement, then signs it off. Going concern, impairment, provisions, estimates, the final view that the accounts are true and fair: none of these are waiting to be looked up. The human is the source of truth.
This is the high-value, liability-bearing work that justifies the signature in the first place. So agents don’t produce the judgement, and they shouldn’t. They handle everything around it, pulling the data, assembling the comparables, flagging anomalies, drafting the rationale and building the trail, then hand a prepared decision to the person who makes the call. The agent compresses the preparation, not the judgement.
Why review matters
Nobody’s ready to let agents run unsupervised, and they shouldn’t have to. The review step is a deliberate, core part of how a well-designed agentic workflow operates.
It works because of where the agent stops. The human still owns the part that was never verifiable to begin with, so the reviewer can check the documented logic and traceability on the mechanical work, then focus their judgement on the parts that were always theirs.
That’s why agents belong inside the review and control processes firms already have. Embedding them in a platform like Silverfin, which has review and sign-off built in, is what makes that practical. The agent does the work, the platform gives the accountant the context, the trail and the controls to decide and stand behind it.
Where we go from here
Done well, agentic AI in accounting takes the preparation off people’s plates so they can spend their time on the work that genuinely needs them. The judgement stays where it belongs, with the accountant. That’s what we’re building towards, and over the coming months we’ll show you how.
