Can Small AI Agents Work Like a Finance Team? I Tried It.

Source: Dev.to

Developing Invoice Shield

Creating an AI Workflow from a Boring Finance Task

If you’ve ever worked with invoices, you know the pain: long hours, repetitive checking, and the constant concern that a minor error could turn into a major financial loss. It is the kind of labor that exhausts people but, somehow, seems ideal for machines.

Rather than creating “yet another chatbot,” I wanted to build something that did more than just talk about it. That’s how Invoice Shield started—as an experiment to investigate whether a team of small, specialized AI agents could manage aspects of a financial workflow the same way people do. Not flawlessly, but intelligently.

The Real Function of Invoice Shield

1. A little multi‑agent system called Invoice Shield mimics a financial team.
2. One agent cleans the incoming invoice data.
3. Another researches probable fraud trends.
4. A fourth assesses how suspicious each invoice looks.
5. A fifth verifies if the score is reliable.
6. A sixth composes a report.
7. A seventh communicates the summary.
8. All steps are orchestrated by a “manager agent” that runs them in order.
9. The system does not discuss theory or philosophy; it simply does the job.
10. It works even when the task is messy or unpredictable.

Reasons for Selecting Agents Instead of One Big Model

“Analyze this invoice,” I might say to a single model, but that often yields ambiguous responses and delusions. In the real world, there are rarely just one question and one answer; it’s iterations, checks, and handoffs—similar to a team.

I therefore divided the behavior into discrete, targeted roles, each with a specific task:

• Investigator – researches fraud trends.
• Scorer – evaluates the suspiciousness of an invoice.
• Validator – checks the reliability of the score.
• Reporter – writes the final report.

The agents converse with one another, making the process feel less like “AI answering a question” and more like AI conducting a procedure.

The Most Interesting Part: Learning to Loop

The fraud‑detection component was the most enjoyable. The agent assesses an invoice several times, modifying its assessment with each pass rather than a single evaluation. Until a different “checker agent” is satisfied, it does not raise an alarm.

• Sometimes the score is low, and the system silently tries again.
• Occasionally it decides, “Okay, something’s seriously wrong here.”

There’s something strangely human about a computer doubting itself, trying again, and only escalating when confident. While not statistically accurate, the approach is plausible in terms of workflow.

What I Learned Building This

• A good AI system is often composed of several simple components working together rather than a single massive model.
• Iteration is preferable to immediate clarity, especially when making difficult judgments.
• Results are clearer and easier to understand when agents specialize.
• Avoiding confident nonsense is easier when grounding decisions with external data (e.g., via Google Search).
• Sophisticated activities don’t need sophisticated code; they need smart structure.

The Result: More Than Just a Score

At the pipeline’s conclusion, Invoice Shield produces a succinct description of what transpired, why the invoice was suspicious, and what action should be taken next. It does not claim to be a flawless fraud detector.

What Comes Next

The system is currently simulated—no banks, no PDFs, no actual money. However, the framework is ready for:

• OCR scanning of invoices
• Integration with actual vendor databases
• Reconciliation supported by SQL
• PDF case reports

If someone wanted to take this into production, the architecture would not need to change—only the inputs. That’s the cool part.

Why This Matters

We hear a lot about “AI replacing jobs.” The intriguing future is when AI joins teams and handles tedious, repetitive tasks, freeing people to focus on strategic, creative work. Although Invoice Shield is small, unrefined, and experimental, it offers a window into that realm. It demonstrates that AI can be effective when it works as a collaborative teammate rather than a simple question‑answering tool.

Code

The entire system is available here if you are interested: https://github.com/MilindGarge07/InvoiceShield