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

Function calling in AI agents is the ability for a model to choose and invoke predefined software functions instead of only returning text. In practice, it lets an AI agent ask your systems to fetch data, run workflows, or take actions using structured inputs and outputs.

How It Works

Think of function calling like a bank teller with a strict playbook.

The teller does not improvise when you ask for a balance, block a card, or open a dispute. They follow a defined set of actions, collect the required fields, and route the request to the right internal system. An AI agent with function calling works the same way: it reads the user’s intent, picks the right function from an allowed list, fills in arguments, and hands control to your application.

A simple flow looks like this:

• •User asks: “What’s my available balance on my business account?”
• •The model recognizes this is not a text-only answer
• •It selects a function like get_account_balance(account_id)
• •Your backend executes that function against core banking APIs
• •The result returns to the model
• •The model formats the final response in plain language

The key point: the model is not directly “doing” banking logic. It is orchestrating calls to trusted systems you already control.

For CTOs, this matters because function calling creates a clean boundary between language understanding and business execution. The model handles ambiguity in human language. Your code handles permissions, validation, audit logging, and transaction safety.

Here’s the mental model:

This is why function calling is safer than letting a model free-write responses for operational tasks. You can constrain what it can do, what parameters it can send, and which systems it can touch.

Why It Matters

CTOs in fintech should care because function calling turns AI from “chat” into an execution layer.

• •

  It reduces hallucination risk

  • •The model does not need to invent balances, policy details, or KYC status.
  • •It asks your systems for truth instead of guessing.
• •

  It makes AI useful inside regulated workflows

  • •You can require explicit functions for actions like card freeze requests, beneficiary checks, or claim lookups.
  • •That gives you an audit trail and clearer control points.
• •

  It improves product UX without exposing backend complexity

  • •Users can say things naturally: “Move $500 to savings” or “Check if this claim is approved.”
  • •The agent translates that into structured operations your systems understand.
• •

  It helps engineering teams build guardrails

  • •You can whitelist functions.
  • •You can validate arguments before execution.
  • •You can block high-risk actions unless authentication or approval rules are met.

For fintech specifically, function calling fits well where there is a mix of natural language input and deterministic business rules. That includes customer support automation, internal ops copilots, fraud triage assistants, claims handling, onboarding flows, and treasury tools.

Real Example

Let’s take a retail banking support assistant.

A customer types: “My debit card was stolen last night. Freeze it and tell me if my last three transactions were declined.”

Without function calling, an LLM might generate a helpful-sounding answer but cannot safely act on anything. With function calling, the agent can follow a controlled workflow:

1. •Identify intent: card freeze + transaction inquiry
2. •Ask for authentication if needed
3. •Call freeze_card(card_id)
4. •Call get_recent_transactions(account_id, limit=3)
5. •Return results in human-readable form

Example schema:

{
  "functions": [
    {
      "name": "freeze_card",
      "description": "Temporarily disable a debit card",
      "parameters": {
        "type": "object",
        "properties": {
          "card_id": { "type": "string" },
          "reason": { "type": "string" }
        },
        "required": ["card_id", "reason"]
      }
    },
    {
      "name": "get_recent_transactions",
      "description": "Fetch recent account transactions",
      "parameters": {
        "type": "object",
        "properties": {
          "account_id": { "type": "string" },
          "limit": { "type": "integer", "default": 3 }
        },
        "required": ["account_id"]
      }
    }
  ]
}

Execution pattern:

if intent == "freeze_card":
    assert user_is_authenticated()
    result = core_banking.freeze_card(card_id=card_id, reason="stolen")
    audit.log("freeze_card", user_id=user.id, card_id=card_id)

if intent == "get_recent_transactions":
    txns = ledger_api.get_transactions(account_id=account_id, limit=3)

What the customer sees:

Your card has been frozen.
Your last three transactions were all declined after 10:42 PM last night.

That response came from real system calls, not model memory.

For insurance teams, the same pattern applies to claims status checks or document requests. A user says: “What’s missing from my claim?” The agent calls get_claim_status(claim_id) and list_missing_documents(claim_id), then explains the next step clearly.

Related Concepts

• •

  Tool use

  • •Broader term for letting models call external systems.
  • •Function calling is one structured version of tool use.
• •

  Agent orchestration

  • •How multiple steps are chained together across tools, prompts, and policies.
  • •Useful when one user request requires several backend calls.
• •

  Structured outputs

  • •Models return JSON or schema-constrained data.
  • •Often used alongside function calling to reduce parsing errors.
• •

  Guardrails

  • •Validation rules that prevent unsafe or invalid actions.
  • •Critical in regulated environments like payments and insurance.
• •

  Retrieval-Augmented Generation (RAG)

  • •Pulls relevant documents into context before answering.
  • •Good for policy questions; function calling is better for taking action against live systems.

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.