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

Function calling in AI agents is the ability for a model to choose and invoke a predefined tool or API instead of only generating text. In practice, it lets an AI agent turn a user request like “check my balance” or “raise a claim” into a structured action your systems can execute.

How It Works

Think of function calling like a bank branch manager handing a customer request to the right internal desk.

The model does not “do the work” itself. It reads the request, decides which function fits, and returns structured arguments for that function. Your application then validates the request, executes the API call, and sends the result back to the model if more reasoning is needed.

A simple flow looks like this:

• •User asks: “What’s my available credit limit?”
• •The model identifies that it needs account data.
• •It returns something like get_credit_limit(customer_id=12345).
• •Your backend calls the credit service.
• •The result comes back to the model.
• •The model responds in plain English: “Your available credit limit is $8,200.”

For engineering managers, the key point is this: function calling is not free-form automation. It is controlled orchestration between an LLM and your internal systems.

Here’s the mental model I use:

This matters because fintech systems need determinism around actions. You do not want an LLM inventing account numbers, fabricating policy rules, or guessing transaction outcomes. You want it to select from approved functions with known inputs and outputs.

In implementation terms, function calling usually means you define:

• •Function name
• •Description
• •Input schema
• •Allowed parameters
• •Validation rules

Example schema:

{
  "name": "get_account_balance",
  "description": "Fetches available and current balance for a customer account",
  "parameters": {
    "type": "object",
    "properties": {
      "customer_id": { "type": "string" },
      "account_id": { "type": "string" }
    },
    "required": ["customer_id", "account_id"]
  }
}

The model sees that definition and decides whether to call it. If it does, your application receives structured JSON instead of vague prose.

Why It Matters

Engineering managers in fintech should care because function calling changes how AI fits into production systems.

• •

  It turns chat into action

  • •Without function calling, an agent can only explain things.
  • •With it, the agent can initiate workflows like balance lookup, KYC checks, claim status retrieval, or payment initiation.
• •

  It reduces hallucination risk

  • •The model no longer needs to “make up” answers when data exists in internal systems.
  • •The source of truth stays in your APIs, not in generated text.
• •

  It creates auditability

  • •Every tool call can be logged with inputs, outputs, timestamps, and user context.
  • •That gives compliance teams something they can review.
• •

  It supports safer automation

  • •You can require human approval before high-risk actions like fund transfers or policy changes.
  • •That gives you guardrails without blocking useful automation.

For fintech teams, this is where AI becomes operationally useful. A chatbot that only summarizes FAQs is nice. An agent that can retrieve verified data from core systems and trigger controlled workflows is something you can put into real customer operations.

Real Example

Say you run digital support for a retail bank. A customer asks:

“My debit card was charged twice for the same transaction. Can you check and open a dispute?”

A function-calling agent could handle this in stages:

1. •Identify the account and recent card transactions.
2. •Match duplicate authorization or settlement events.
3. •Check whether the transaction qualifies for dispute under policy.
4. •Create a dispute case if eligible.
5. •Return case number and next steps to the customer.

Possible functions might include:

{
  "name": "search_card_transactions",
  "parameters": {
    "customer_id": "string",
    "card_last4": "string",
    "date_range_days": "number"
  }
}

{
  "name": "create_dispute_case",
  "parameters": {
    "transaction_id": "string",
    "reason_code": "duplicate_charge",
    "customer_id": "string"
  }
}

The agent does not decide policy on its own. It uses your rules engine or backend services to determine eligibility. That separation is important in regulated environments.

A good production pattern looks like this:

• •LLM interprets intent
• •Backend validates identity and permissions
• •Rules engine checks policy eligibility
• •Function executes only if all checks pass
• •Human review kicks in for exceptions

That gives you automation without handing business logic over to the model.

Related Concepts

• •

  Tool use

  • •Broader term for letting models interact with external systems beyond text generation.
• •

  Agent orchestration

  • •The control layer that decides which tools to call, in what order, and when to stop.
• •

  JSON schema validation

  • •Critical for making sure model-produced arguments are well formed before execution.
• •

  Guardrails

  • •Policy controls that restrict unsafe actions, sensitive data exposure, and unauthorized operations.
• •

  Human-in-the-loop approval

  • •Required for high-risk flows like payments, claims approvals, underwriting overrides, or account closure.

Function calling is the bridge between language models and real business systems. For fintech teams, that bridge only works when it is tightly governed: clear schemas, strict validation, auditable logs, and explicit approval paths where risk demands it.

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.