The Anatomy of an AI Agent: How Nilus Thinks Across Your ERP

In finance, every number has to tie back to a source - like a bank transaction or an ERP record - and every change needs a clear explanation. That’s what makes reconciliation defensible during close and audit.

Most workflows today still rely on rule-based matching, spreadsheets, or scripted automation layered on top of ERPs, sometimes supported by heuristics or machine learning. They work well when inputs line up.

The problem is they’re built around consistency. 

When a payment comes in short, or one transaction spans multiple invoices, that structure starts to break. Instead of resolving the ambiguity, the workflow pushes it to manual review. Your team is left reconstructing what happened, often under serious time pressure.

Treasury data rarely arrives clean. Payments get split across accounts, fees alter expected amounts, and key context often sits outside the ERP.

At that point, your limitation isn’t speed - it’s how you make decisions. Systems that depend on exact matches or predefined logic can’t fully account for how records relate when the signals are incomplete.

How does agentic AI work in finance?

Agentic AI in finance works by evaluating relationships across financial data - such as transactions, references, counterparties, and historical patterns - to determine what likely happened and apply those decisions with a clear audit trail.

Instead of relying only on exact matches, it can work through cases like partial payments or missing references while keeping each step tied back to source data so the outcome remains verifiable.

So the real question isn’t whether these systems can automate a task. It’s whether the output holds up when the data doesn’t.

This is the model Nilus follows: if the inputs don’t support a clear, traceable decision, the system stops and surfaces the discrepancy. If they do, it proceeds. The easiest way to see how this works is to break it down into three parts: how it reads data, how it determines what matches, and how it applies those decisions.

The senses: Multimodal data ingestion

How the Nilus agent reads your financial data

A Nilus agent starts by building a live cash view directly from your systems:

• Bank accounts and payment providers: Balances, transactions, timestamps, references
• ERPs: Invoices, customer and vendor records, GL data
• Supporting inputs: Remittance details and payment notes

This data flows in through direct bank connectivity and API integrations, updating as real-time cash activity comes in from each source. 

Instead of collecting data across systems and stitching it together manually, Nilus connects each transaction to its related records upfront. At the transaction level, you’re looking at the bank movement alongside the corresponding ERP record and the reference data linking them.

That means each transaction is reviewed with its full context, not as an isolated line item.

The logic: Contextual reasoning

How the Nilus agent determines what matches and why

Once the Nilus agent has the full picture of your financials, it evaluates how each transaction should be handled.

This is where the system shifts from matching data to making decisions.

Each decision is based on inputs such as:

• transaction amount and timing
• reference fields (invoice numbers, wire details, memos)
• counterparty identity
• prior transactions and recorded payment patterns

These inputs are evaluated together to determine which transaction settles which invoice, whether the payment is full or partial (or spans multiple invoices), and whether any differences in amount can be explained by fees, discounts, or timing.

Rule-based systems depend on predefined matching rules and often break when inputs don’t align closely enough. Like when references are incomplete or amounts don’t align, those transactions move to manual review.

A Nilus agent works through those cases by using patterns from past transactions and known relationships, rather than relying only on exact rule matches.

For example:

• If a $48,500 payment comes in against a $50,000 invoice, the agent checks prior payments from the same customer and identifies a consistent fee deduction
• If a wire reference doesn’t match the invoice number, the agent maps the reference to a known customer or project using ERP records
• If one payment covers multiple invoices, the agent groups invoices based on amount, timing, and counterparty relationship

For every outcome, you can see the source transaction, the records evaluated, how they were linked, and why the match was accepted or rejected.

If the inputs don’t support a clear decision because of, say, missing references or no supporting history, the agent flags the transaction for review, along with the discrepancy it found.

Once a decision is made - either by the system or your team - the next step is applying it.

The hands: Autonomous execution

How the Nilus agent applies decisions within your connected systems

When a transaction has a clear, defensible outcome, the Nilus agent completes the follow-through.

That involves three actions:

• Applying the match: Link the bank transaction to the correct invoice or ERP record.
• Classifying the transaction: Categorize transactions based on your cash structure and historical behavior.
• Updating the cash position: Reflect the change in your balances, cash position, and reporting as your connected systems update.

Because these steps happen in the single flow, the update is applied immediately to your current cash view. This removes the need for separate manual updates across systems and reduces the amount of rechecking required.

In a typical workflow, this process is fragmented and manual. Someone identifies a match, updates the ERP, adjusts reports, and then verifies the numbers. Each step adds delay and creates opportunities for inconsistency.

Note that execution is designed to happen only when the underlying logic meets defined traceability and confidence thresholds. If the Nilus agent can’t connect a transaction to the right records, it stops and flags it for review with the relevant inputs and discrepancies attached.

Where you stay in control (Human-in-the-Loop)

Human-in-the-loop refers to the system handling transactions where the data supports a clear decision, while surfacing the rest for your review.

In Nilus, those transactions come with context already attached - the bank transaction, the related records, and the specific mismatch.

You analyze that context and decide how the transaction should be handled. The system then applies your decision directly, without additional updates or rechecks.

Over time, similar cases are handled faster as the system builds on past outcomes.

Your role centers on decision-making - reviewing exceptions, applying judgment, and defining how ambiguous cases should be handled going forward.

Nothing is applied to your books unless it meets your standards for a valid, supported decision. When it doesn’t, the system surfaces it for your review.

Run a sample of your transactions through Nilus and see what gets handled automatically and what requires review. This will help determine if the agent aligns with your standards for control, traceability, and accuracy.

FAQs on agentic AI in finance

What is agentic AI in finance?

Agentic AI in finance is a system that can evaluate financial data, make traceable decisions, and complete parts of workflows automatically.

It can work through mismatches like partial payments or unclear references and produce a result your team can review, rather than relying only on strict rule-based matching.

How does explainable AI reduce black-box risk?

Explainable AI reduces black-box risk by making each result traceable to the data and logic behind it.

You can see which records were used, how they were compared, and why a transaction was matched or flagged. Your team can verify outputs during close or audits without retracing the entire process.

Can AI agents handle unstructured data like email invoices?

Yes. Depending on the system’s extraction capabilities, AI agents can pull details from unstructured sources like PDFs, remittance emails, and payment notes, then connect that information to transactions and ERP records.

This lets the system use details that don’t show up in bank or ERP data, which helps it handle deductions, split payments, and missing references more accurately.