How to Build Your Collections Optimizer Agent

• Microsoft Copilot Studio
• AWS Bedrock
• Google Gemini Enterprise
• Salesforce Agentforce
• Cursor — the walkthrough below uses Cursor; Claude Desktop works the same way
• More platforms being added as they gain adoption

You're not locked in — the same underlying intelligence connects to whichever builder your organization runs on.

Step 2: Add a Data Source

Every agent builder needs to know where your customer data lives. There are two ways to connect it:

Step 3: Choose MCP Where Possible

CSV upload works, but MCP is the preferable path — it's the secure, standardized way for an agent to access live data, rather than a static file that goes stale the moment you export it.

Steps to Enable MCP on AI Platforms

Say you run collections at an NBFC with 12,000 overdue accounts and 40 agents. You export the spreadsheets you already have: your overdue accounts, their payment history, and the log of past calls and messages. Every night, without anyone re-sorting a list, the MCP re-scores your whole book:

1. Every account gets a live score. How likely it is to pay, how likely it is to pay without being contacted, and what you can expect to recover. Based on actual payment behavior, not just how old the debt is.
2. Your contact rules are set as hard limits. You enter your permitted contact hours and frequency caps once. The agent will never recommend a contact outside them, and logs every recommendation so you have an audit trail.
3. Our AI Agent finds the accounts a simple sort would miss. Some accounts will pay without a call, some won’t pay whatever you do, and some will only pay if reached at the right time. It tells these apart and puts the third group at the top.
4. An agent asks our AI Agent a plain question. In Cursor, Copilot Studio, or wherever the agent lives, they type: “Who should I work first this morning?” It queries the MCP in real time and answers: “Start with account 4821. Call between 6 and 8 pm. Moderate payment history, high response to calls. Skip account 4822, likely to pay on its own this week.”
5. Our AI Agent explains itself when asked. The agent follows up: “Why is 4821 ahead of 4822?” It doesn’t deflect. It shows the why: a promise kept last month, a good time window for reaching this customer, and a larger expected gain from contact than the other account.
6. Your team acts — today, not next month. A queue built by our AI Agent instead of oldest-first sorting is the difference between agents who spend the day on accounts that were going to pay anyway, and agents who spend it where a contact makes the difference. Team leads can also assign accounts to the agents most likely to succeed with them, and your recovery head gets a roll-rate forecast for provisioning.

That’s the shift: from an overdue list agents have to work through blindly, to a ranked plan for every agent — so you know exactly which account is worth the next call.

“Here’s our overdue accounts, payment history and contact log — who should each agent work first today?”

“Which accounts are likely to pay on their own this week, so we can leave them alone?”

“Forecast how many accounts will roll into the next bucket next month, and which agents should handle the highest-recovery cases.”