Key facts
| Default mode | Read-only: analyse, reconcile, categorise, draft |
| Approvals | Two-person control for payments, journal entries and limit changes |
| Audit | Immutable, timestamped logs of every retrieval, draft and decision |
| Limits | Hard caps on amounts, volumes and counterparties enforced in code |
| Reconciliation | The agent flags mismatches; humans resolve them |
| Data boundary | Region-locked VPC, on-prem or air-gapped deployment |
| Model routing | Structured extraction on small models, reasoning on frontier |
| Status | Function calling, JSON mode, embeddings and audit logs are live |
TL;DR
- The agent prepares and reconciles; humans approve and move money.
- Enforce amount, volume and counterparty limits in code, not in prompts.
- Two-person approval plus immutable logs are the non-negotiables.
- Read-only by default; any write capability is narrow and separately approved.
- Keep regulated data in an approved region or on-prem boundary.
How it works, step by step
- Map the workflows and mark where money, balances or reported figures can change.
- Start read-only: reconciliation, anomaly detection, categorisation and draft entries.
- Enforce hard limits on amounts, frequency and counterparties in code.
- Add two-person approval for every action that moves or commits money.
- Write immutable audit records linking source documents, drafts and approvals.
- Validate outputs against control totals before anything reaches a ledger.
- Pilot in one workflow, with daily reconciliation and a rollback plan.
Try it yourself
Open the AI agent cost calculator →
The rule: agent prepares, humans approve
Finance is where agent enthusiasm meets consequence. A mispriced refund or a duplicated payment is not a model-quality issue; it is a financial event with reporting and regulatory follow-up. The workable division is clear: the agent does the reading, matching, categorising and drafting, and an authorised human approves anything that changes the books or moves funds.
This is not a limitation of current models so much as a control requirement. Even a highly accurate agent needs a control environment auditors can inspect, and separation of duties is the foundation of that environment.
Controls that regulators look for
- Separation of duties: the identity that prepares an action is not the identity that approves it.
- Limits: amount, velocity and counterparty caps enforced outside the model.
- Immutability: append-only logs with approver identity, timestamps and source references.
- Reversibility: defined reversal procedures for every automated action.
- Data boundaries: regulated data stays in an approved region or on-prem.
- Monitoring: alerts on limit breaches, unusual patterns and failed reconciliations.
Where agents genuinely help in finance
The high-value work is tedious and text-heavy: matching invoices to purchase orders, extracting terms from contracts, flagging duplicate payments, categorising transactions, and preparing reconciliation summaries. These tasks are repetitive, verifiable and currently consume analyst time disproportionate to their value.
Track accuracy with control totals, not vibes: dollars reconciled, exceptions raised, time saved per cycle and reversal rate. On Plugsky, function calling, JSON mode, embeddings and audit logging are live, with region-locked deployment plus VPC, on-prem and air-gapped options for regulated data. 30+ models on one key let extraction run on a cheap model while reasoning uses a frontier one. Plans are on the live pricing page; batch endpoints are coming soon for high-volume cycles.
Honest comparison
| Finance task | Agent role | Control | Failure impact |
|---|---|---|---|
| Invoice matching | Match and flag exceptions | Read-only plus sampling | Payment delay or duplicate |
| Contract term extraction | Extract and cite clauses | Schema validation | Wrong obligation recorded |
| Transaction categorisation | Suggest categories | Review threshold | Misstated accounts |
| Draft journal entries | Prepare entry | Two-person approval | Books misstated |
| Payments and transfers | Must not execute | Dual control, hard limits | Financial loss |
Frequently asked questions
Can an AI agent move money?
It should not execute payments autonomously. Prepare, validate and propose, with dual control and hard limits enforced outside the model for execution.
How do we satisfy auditors?
Show separation of duties, immutable logs, enforced limits, reconciliation evidence and a documented reversal path. Design controls before deployment, not during audit.
What should we automate first?
Invoice matching, contract data extraction and anomaly flagging. They are repetitive, verifiable and already have control totals to check against.
How do we prevent duplicates?
Idempotency keys on every action, reconciliation against the ledger before and after, and velocity limits that surface repeated attempts.
Can it run on-prem?
Yes. Plugsky supports region choice plus VPC, on-prem and air-gapped deployment, so regulated financial data can stay inside your boundary.
How do we measure success?
Dollars reconciled, exception rate, analyst hours saved per cycle, reversal rate and audit findings. Review them together.
What about model accuracy claims?
Do not rely on vendor claims. Build a labelled evaluation set from your own documents and monitor accuracy per task before and after every change.