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

Tool use in AI agents is the ability for an agent to call external functions, APIs, or systems to get work done instead of only generating text. In lending, tool use means the agent can check a loan status, pull a credit policy, calculate affordability, or create a CRM note by invoking the right system at the right time.

How It Works

Think of an AI agent as a loan officer with access to a desk full of systems.

Without tools, that loan officer can talk, explain, and draft messages. With tools, they can also open the LOS, query the CRM, fetch pricing rules, and run an income verification check. The agent decides when it needs outside data or an action, calls the tool, then uses the result to continue the conversation or complete the task.

The flow is usually:

• •User asks something like: “Can this applicant qualify for a £180k mortgage?”
• •The agent inspects the request and realizes it needs facts it does not have.
• •It calls tools such as:
  • •get_applicant_profile(applicant_id)
  • •calculate_affordability(income, debts, term)
  • •fetch_product_rules(product_code)
• •The tools return structured data.
• •The agent combines that data with policy and language generation to produce a useful answer.

A good analogy is a lending operations analyst with browser tabs open. They are not “thinking harder” to know the current rate card or borrower history. They are looking it up in systems of record, then making a decision based on what they find.

For developers, the important part is that tool use is not free-form text generation. It is controlled execution.

A typical implementation has three pieces:

In production lending systems, tools should be narrow and deterministic. The agent should not invent underwriting rules; it should fetch them from policy services or rule engines. If your bank already has APIs for credit decisioning, document verification, KYC checks, or case management, those are natural tool candidates.

Why It Matters

• •

  It reduces hallucinations

  • •The agent can verify facts against source systems instead of guessing rates, balances, or policy rules.
• •

  It turns chat into action

  • •A borrower-facing assistant can do more than answer questions. It can trigger status checks, open support tickets, or prefill application forms.
• •

  It fits regulated workflows

  • •Lending teams need auditability. Tool calls create logs showing what was queried, when it was queried, and which system produced the answer.
• •

  It improves operational efficiency

  • •Agents can handle repetitive tasks like retrieving loan milestones, summarizing missing documents, or checking exception reasons before handing off to humans.

For lenders specifically, tool use is where AI becomes operationally useful. A model alone can draft polite responses. A model with tools can actually move cases forward.

Real Example

A mortgage servicing team wants an assistant that helps brokers answer borrower questions about application status.

The user asks: “What’s holding up loan application 78421?”

The agent does not guess. It uses tools:

1. •get_loan_application(78421)
2. •list_missing_documents(78421)
3. •get_recent_case_notes(78421)

The tools return:

• •Application stage: “Underwriting review”
• •Missing document: “Updated payslip for June”
• •Recent note: “Awaiting employer verification”

The agent then replies:

Your application is currently in underwriting review. The main blocker is one missing document: an updated June payslip. There’s also an outstanding employer verification step. Once those are complete, the file can move forward.

That is tool use in practice: the model interprets the request, pulls live data from systems of record, then turns that into a clear response.

In a more advanced setup, the same agent could also:

• •Create a task in Salesforce for the broker
• •Send a secure document request link
• •Update the case status after verification completes

That’s where tool use starts crossing from assistant behavior into workflow automation.

Related Concepts

• •

  Function calling

  • •The mechanism many LLM platforms use to let models request structured actions from external code.
• •

  RAG (Retrieval-Augmented Generation)

  • •Pulling documents or policy text into context so answers reflect internal knowledge bases instead of model memory alone.
• •

  Agent orchestration

  • •Managing multi-step workflows where an agent chooses between tools, retries failed calls, and hands off when needed.
• •

  Guardrails

  • •Constraints that prevent unsafe actions like changing loan terms without approval or exposing sensitive PII.
• •

  Human-in-the-loop review

  • •Keeping underwriters or ops staff in control for decisions that require judgment or regulatory oversight.

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.