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

🛡️ What We Created

A lightweight, multi-agent system called InvoiceShield mimics how a finance team assesses invoices for risk.
Rather than relying solely on one AI, we developed several tiny agents that:
Organize invoice-style information.
Examine the risk of vendors.
Score anomalies repeatedly.
Confirm your confidence.
Write investigation summaries.
Communicate outcomes.
It is not a single prompt but rather a process model.

🤝 Why This Problem

Finance jobs are repetitious, error-prone, and high-stakes:
· Invoices that are duplicates
· Unknown merchants
· Manual reconciliation
· Unexplained decisions
Our goal was to determine whether several little AI agents might divide labor into distinct tasks, exactly like people do.

🧠 Architecture Snapshot

The interactive_finops_agent, which powers a pipeline of:
· data_ingest_agent: arranges input;
· research_agent: utilizes Google_search;
· anomaly_detector: loop-based scoring;
· validation_checker: threshold gate;
· investigation_agent: reports results;
· communications_agent: produces summary
Each agent does a single task effectively. When combined, they yield an understandable outcome.

🔄 The Loop: Small but Powerful

The anomaly detector doesn’t guess once—it:
· Scores the invoice
· Checks confidence
· Repeats until threshold is satisfied
This makes decisions deliberate, not spontaneous.
Occasionally:
“Retry, confidence too low.”
Occasionally:
“Escalate, looks suspicious.”

💡 What We Learned

· Workflows involving multiple agents are more dependable than those involving a single mega-agent.
· Orchestration plus small roles equals scalable logic
· Iteration is a straightforward yet powerful tactic.
· Hallucinations are lessened by grounding.
· If you design for explainability, it is simple.
· "Genius AI" is not what it is. There is a small office crew that never gets bored.

🌟 Why It’s Interesting

InvoiceShield demonstrates the following capabilities of autonomous agents:
· Divide up the cognitive work
· Verify decisions
· Produce results that can be explained
Not ostentatious, but somewhat similar to actual corporate automation.

What We Learned from the 5-Day AI Agents Intensive

Day 1: Basics We learned what makes an AI system an agent: perception, reasoning, action, and autonomy. We investigated the significance of multi-agent systems for practical processes. Conclusion: Agents act like independent workers rather than chatbots.
Day 2: Resources & Activities We looked at how agents act outside of language using tools, APIs, and the Model Context Protocol. Conclusion: When agents are capable of doing more than just talking, they become valuable.
Day 3: Context & Memory We researched short-term and long-term memory and how context supports multi-step activity. Conclusion: Memory transforms agents into strategic systems rather than reactive responses.
Day 4: Quality and Assessment We gained knowledge about how to log, track, and assess agents for dependability, security, and openness. Conclusion: Additionally, smart agents need to be consistent, auditable, and traceable.
Day 5: From Prototype to Production We examined agent deployment, scalability, agent supervision, and agent-to-agent communication. Conclusion: Converting a demo into a reliable, functional solution is challenging.

⭐ Overall Learning

We discovered how to create agents that act, think, and use tools.
Keep in mind to integrate the performance scale into actual workflows.
Production-oriented agent systems replaced LLM prototypes.

🧠 Mindset Shift

Agents aren’t merely better chatbots. They are autonomous, cooperative systems that can accomplish worthwhile tasks.

Team:

1. Milind Garge
2. Ayush Malwatkar

Link to Code

GitHub Repository:
👉 https://github.com/MilindGarge07/InvoiceShield

Demo Video
YouTube Link:
👉 https://youtu.be/qEoCgYzlcGM

🙏 Closing Thoughts

We designed InvoiceShield to explore a simple idea: “What if AI worked like teammates—not oracles?”
Even with simulated data, the system feels structured, careful, and collaborative.
Feedback, suggestions, or enhancements would be greatly appreciated, particularly from those developing in the agent space.
I appreciate you reading!
— Milind and Ayush