How FinAgent-Orchestrator Uses AI Agents to Handle Payables & Receivables Automatically

Introduction

Managing finance-related queries — especially payables invoices and receivables transactions — can quickly become repetitive and time-consuming.

That’s where FinAgent-Orchestrator (AA_AGENT_TEAM_SUPERV_01) comes in.

This project is a workflow-driven multi-agent system designed to intelligently understand user intent and automatically delegate tasks to the right agent. Instead of building one massive logic-heavy system, this architecture uses a Supervisor Agent + Specialized Worker Agents approach.

Let’s break it down in simple, human language 👇

System Architecture (Simple & Clear)

At the heart of this system is a Supervisor Agent that acts like a manager.

It listens to the user’s request and decides:

  • “Is this about a Supplier?”

  • “Is this about a Customer?”

Then it assigns the job to the correct worker agent.

The Agents

Agent

Role

What It Does

AA_AGENT_SUPERV_01

Supervisor

Detects intent & delegates tasks

AA_AGENT_AP_INV

Payables Worker

Retrieves invoice data using Supplier name

AA_AR_AGENT_INV

Receivables Worker

Retrieves transaction data using Customer name

Think of it like this:

🧑‍💼 Supervisor = Team Manager
👨‍💻 Worker Agents = Finance Specialists

How the Workflow Actually Works

Here’s the step-by-step flow:

1️⃣ User Sends a Request

Example:

  • “Show invoices for ABC Suppliers”

  • “Get transactions for XYZ Customer”

2️⃣ Supervisor Detects Intent

  • If the message mentions Supplier → send to Payables Agent

  • If it mentions Customer → send to Receivables Agent

3️⃣ Worker Agent Fetches Data

Using REST API endpoints:

For Payables

/fscmRestApi/resources/11.13.18.05/invoices?q=Supplier='{SupplierName}'

For Receivables

/fscmRestApi/resources/11.13.18.05/receivablesInvoices?finder=invoiceSearch;BillToCustomerName="{CustomerName}"

Response is Generated

The agent:

  • Retrieves structured data

  • Summarizes it

  • Sends back a professional, friendly message

Project Structure Explained

The repository is clean and modular — making it easy to scale.

AA_AGENT_TEAM_SUPERV_01/
│
├── config/
│ └── workflow.json
│
├── src/
│ ├── main.py
│ ├── agents/
│ ├── tools/
│ └── utils/
│
└── tests/

Important Files

File

Purpose

workflow.json

Defines agents & delegation logic

supervisor_agent.py

Core decision logic

ap_invoice_agent.py

Handles payables data

ar_transaction_agent.py

Handles receivables data

http_client.py

Shared REST API helper

test_workflow.py

Validation tests

This structure follows modular agent architecture, making it easy to:

  • Add new agents

  • Add new workflows

  • Expand use cases

Running the Project Locally

1️⃣ Clone the Repository

git clone https://github.com/<your-org>/AA_AGENT_TEAM_SUPERV_01.git
cd AA_AGENT_TEAM_SUPERV_01

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Application

python src/main.py

(Optional) If Using FastAPI

uvicorn src.main:app --reload

Run Tests

pytest tests/

Smart Error Handling

The system includes:

Email-based error handling

Configurable recipients in workflow.json

Graceful fallback responses

If a query falls outside supported categories, the system responds politely rather than crashing.

System Limitations (By Design)

Currently, the Supervisor supports:

  • Supplier-related queries

  • Customer-related queries

It treats:

  • “Invoice”

  • “Transaction”

…as synonyms within supported domains.

Any other type of query results in a clear explanatory message.

Tech Stack

  • Python 3.10+

  • REST API Integration

  • JSON Workflow Configuration

  • Modular Agent Architecture

  • Optional FastAPI Support

Why This Architecture Matters

Instead of writing complex conditional logic inside one giant script, this system:

  • Separates responsibility

  • Improves maintainability

  • Makes scaling easy

  • Follows clean agent-based design principles

This is the foundation of enterprise-ready AI orchestration systems.

Final Thoughts

FinAgent-Orchestrator demonstrates how powerful AI workflows don’t need to be chaotic.

With:

  • A Supervisor for intent routing

  • Specialized Worker Agents

  • Clear workflow definitions

  • Modular architecture

You get a system that is:

  • Structured

  • Scalable

  • Production-friendly

And most importantly — easy to extend.