What is tool use in AI Agents? A Guide for product managers in lending

Tool use in AI agents is the ability for an agent to call external systems, APIs, or functions to get information or take actions it cannot do on its own. In lending, that means the agent can check a credit decision engine, pull loan status from a core system, verify income through a third-party service, or create a task in your CRM.

How It Works

Think of an AI agent as a loan officer with a very sharp brain but no access badges. It can understand the customer’s question, decide what needs to happen next, and then use tools to do the actual work.

A simple example:

• •A borrower asks: “What’s the status of my mortgage application?”
• •The agent reads the request and realizes it needs live data.
• •It calls a tool like get_application_status(application_id).
• •The tool returns the current stage: Underwriting.
• •The agent responds in plain English: “Your application is currently in underwriting. The next update should come after document review.”

The important part is that the model is not guessing. It is choosing when to use a tool, passing the right inputs, and using the output to answer accurately.

For product managers, the easiest analogy is a lending ops dashboard with action buttons. The person using the dashboard does not manually query every system by hand. They click “verify income,” “pull credit,” or “send adverse action notice.” Tool use gives an AI agent those same buttons.

Under the hood, there are usually three steps:

• •Interpretation: the model figures out what the user wants.
• •Tool selection: it decides which function or API should handle it.
• •Execution and response: it reads the tool output and turns it into a usable answer or action.

This matters because AI models are good at language, but weak at real-time facts. Tool use bridges that gap.

Why It Matters

• •

  It reduces hallucinations

  In lending, incorrect answers are expensive. If an agent can check live systems instead of guessing payment status or policy details, you reduce bad customer experiences and operational risk.

• •

  It makes agents actually useful

  A chat interface alone is not enough. Tool use lets an agent do work: retrieve documents, update case notes, trigger callbacks, or route exceptions.

• •

  It improves compliance

  Lending workflows depend on auditability. When an agent uses tools with logged inputs and outputs, you get traceability for decisions and actions.

• •

  It connects AI to your existing stack

  You do not need to rebuild your LOS, CRM, KYC vendor integrations, or document store. Tool use lets AI sit on top of systems you already have.

Real Example

A consumer applies for a personal loan and later asks: “Can I increase my loan amount before disbursement?”

A well-designed AI agent can handle this by using multiple tools:

1. •It checks whether the application is still open.
2. •It pulls current underwriting status from the loan origination system.
3. •It checks policy rules for whether amount changes are allowed at that stage.
4. •If allowed, it creates a case for an underwriter or sends the request into workflow.
5. •It replies to the borrower with the next step.

Here’s what that might look like conceptually:

{
  "user_request": "Can I increase my loan amount before disbursement?",
  "tool_calls": [
    {
      "name": "get_loan_application",
      "input": { "application_id": "APP12345" }
    },
    {
      "name": "check_policy_rule",
      "input": {
        "rule_name": "increase_amount_before_disbursement",
        "loan_type": "personal_loan",
        "status": "approved_pending_disbursement"
      }
    },
    {
      "name": "create_case",
      "input": {
        "queue": "underwriting_review",
        "reason": "Borrower requested higher amount"
      }
    }
  ]
}

The user never sees this orchestration. They just get a clear answer:

“Your request can be reviewed because your loan has not been disbursed yet. I’ve created a case for underwriting to assess whether the amount can be increased.”

That is tool use in practice: reasoning plus action across real systems.

For lending teams, this unlocks several workflows:

• •Application status updates
• •Document collection follow-ups
• •Income and identity verification
• •Policy-based eligibility checks
• •Case creation and handoff to human teams

Related Concepts

• •

  Function calling

  The mechanism many LLMs use to invoke structured tools with defined inputs and outputs.

• •

  Agent orchestration

  How multiple steps are chained together across tools, memory, and decision points.

• •

  RAG (retrieval augmented generation)

  Pulling relevant documents or policy text into context before answering.

• •

  Workflow automation

  Rule-based process execution across internal systems without manual intervention.

• •

  Human-in-the-loop

  Keeping people involved for exceptions, approvals, and regulated decisions where automation should stop.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.