What is function calling in AI Agents? A Guide for product managers in fintech

Function calling is a way for an AI agent to request that your software run a specific action, such as checking an account balance, creating a support ticket, or calculating an insurance premium. It turns the model from a text generator into a system that can trigger real business operations through defined APIs or functions.

In fintech, that matters because the agent stops guessing and starts working with controlled, auditable actions.

How It Works

Think of function calling like a bank teller with a fixed set of buttons behind the counter.

A customer says, “What’s my available balance?” The teller does not invent an answer. They press the “balance lookup” button, pass in the account number, wait for the core banking system to respond, then relay the result.

That is function calling:

• •The user asks something in natural language.
• •The AI agent interprets the request.
• •The model decides whether it needs to call a function.
• •Your application executes that function.
• •The result comes back to the model.
• •The model turns that result into a response for the user.

The key point for product managers: the model does not directly access your systems. You define the available functions, their inputs, and their outputs. That gives you control over what the agent can do.

A simple example:

{
  "name": "get_account_balance",
  "description": "Fetches current available balance for a customer account",
  "parameters": {
    "account_id": "string"
  }
}

If the user says, “Can I afford a $500 transfer right now?”, the agent might call:

{
  "name": "get_account_balance",
  "arguments": {
    "account_id": "ACC12345"
  }
}

Your backend returns something like:

{
  "available_balance": 820.50,
  "currency": "USD"
}

Then the agent replies: “Yes, you have $820.50 available, so a $500 transfer should go through.”

For engineers, this is usually implemented as structured tool use: prompt + tool schema + execution layer + model response. For product managers, think of it as giving the AI a menu of approved actions instead of letting it freestyle.

Why It Matters

• •

  It reduces hallucinations in high-stakes workflows.
  A fintech assistant should not guess balances, fees, policy terms, or payment status. Function calling forces it to retrieve real data before answering.

• •

  It enables actual task completion, not just chat.
  An agent can do more than explain overdraft rules. It can check eligibility, open a ticket, initiate KYC review, or pull claim status.

• •

  It improves auditability and control.
  Every function call can be logged: who requested it, what parameters were used, what system responded. That matters for compliance and dispute resolution.

• •

  It creates cleaner product boundaries.
  Product teams can define exactly which actions are safe for an AI assistant and which ones require human approval or additional authentication.

Real Example

Let’s say you run digital claims support for motor insurance.

A customer types: “My car was hit yesterday. Can I start a claim and check if my policy covers towing?”

The agent can use two functions:

1. •get_policy_details(policy_id)
2. •create_claim(policy_id, incident_date, incident_type)

Workflow:

• •The model detects that it needs policy data before answering coverage questions.
• •It calls get_policy_details.
• •Your policy admin system returns:
  • •active policy = true
  • •towing coverage = included
  • •excess = $250
• •The model then calls create_claim if the customer confirms they want to proceed.
• •Your claims system creates a claim record and returns claim_id = CLM88421.

The final response might be:

Your policy includes towing coverage. Your claim has been started under reference CLM88421. Your excess is $250.

This is useful because the agent is not making up policy interpretation or creating records on its own. It is orchestrating approved business actions across systems.

From a product perspective, this changes how you design journeys:

• •You can reduce handoffs between chat and forms.
• •You can keep users inside one guided flow.
• •You can decide where human review is mandatory.
• •You can measure completion rate by function call success rate.

Related Concepts

• •

  Tool use
  Broader term for letting models interact with external systems through functions, APIs, search engines, or databases.

• •

  Structured outputs
  A way to force model responses into predictable formats like JSON so downstream systems can parse them reliably.

• •

  Agent orchestration
  The logic that decides when to call tools, in what order, and how to handle failures or retries.

• •

  Human-in-the-loop
  A control pattern where sensitive actions like loan approvals or claims payouts require human confirmation before execution.

• •

  API governance
  The rules around authentication, authorization, logging, rate limits, and data access for any tool exposed to an AI agent.

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.