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

Function calling is a way for an AI agent to ask your software to run a specific function, instead of guessing the answer itself. In practice, it lets the model turn a user request into a structured action like check_balance, validate_payment, or create_claim.

For engineering managers in payments, this is the bridge between “chat with the model” and “do something useful in production.” It turns an AI agent from a text generator into a controlled orchestration layer that can read context, decide what tool to use, and hand off work to your existing systems.

How It Works

Think of function calling like a call center agent with a strict playbook.

The customer says, “Can you tell me if my card payment went through?” The agent does not invent an answer. Instead, it looks at the request, chooses the right internal action, and sends a structured instruction to the backend system: check transaction status for this payment ID.

That is what an AI agent does with function calling:

• •The user asks something in natural language.
• •The model interprets intent.
• •The model selects a predefined function from a list you exposed.
• •Your application executes that function against your APIs or services.
• •The result comes back to the model.
• •The model formats the final response in plain English.

The key point: the model is not directly touching your database or payment rails. Your application remains in control.

A simple mental model:

In payments, this matters because the difference between “answering” and “acting” is huge. A model should not freestyle around settlement status, refund eligibility, or chargeback rules. It should call only approved functions with validated inputs.

A typical function definition might look like this:

{
  "name": "get_transaction_status",
  "description": "Fetch the current status of a payment transaction",
  "parameters": {
    "type": "object",
    "properties": {
      "transaction_id": { "type": "string" }
    },
    "required": ["transaction_id"]
  }
}

When the user says, “Where is payment TXN-88421?”, the model may return something like:

{
  "function_name": "get_transaction_status",
  "arguments": {
    "transaction_id": "TXN-88421"
  }
}

Your service then calls the payments API, gets back something like SETTLED, and passes that result back to the model. The model can then say: “Payment TXN-88421 was settled at 14:32 UTC.”

Why It Matters

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

• •

  It reduces hallucination risk
  The model stops pretending it knows ledger state or payout status. It must query real systems for real answers.

• •

  It creates auditability
  You can log which function was called, with what parameters, by whom, and when. That matters for compliance and incident review.

• •

  It supports controlled automation
  You can expose only safe actions like lookup_customer, estimate_fee, or initiate_refund_review instead of giving broad system access.

• •

  It improves user experience without replacing workflow controls
  Agents can handle repetitive support tasks while still respecting approval steps, KYC checks, fraud rules, and limits.

For payments teams specifically, this pattern helps with common operational pain points:

• •Customer support asks about failed transfers
• •Ops teams need quick reconciliation queries
• •Risk teams want guided case triage
• •Finance teams want consistent fee explanations

The manager-level takeaway is simple: function calling lets you add AI on top of existing payment infrastructure without rewriting core systems. You get better automation boundaries and less risk than letting an LLM answer from memory.

Real Example

Say you run an online bank that supports instant transfers. A customer messages support:

“I sent $250 to my contractor two hours ago and it still says pending. Can you check?”

Without function calling, the model might produce a vague explanation about network delays. With function calling, it can do something much more useful.

You expose these functions:

• •get_transfer_status(transfer_id)
• •get_customer_profile(customer_id)
• •get_fraud_hold_reason(transfer_id)
• •create_support_ticket(summary)

The agent first identifies that it needs transfer details. If it already has the transfer ID from chat history or account context, it calls:

{
  "function_name": "get_transfer_status",
  "arguments": {
    "transfer_id": "TRF-102938"
  }
}

Your payments service returns:

{
  "status": "PENDING",
  "reason_code": "BANK_PROCESSING_WINDOW",
  "estimated_release_time": "2026-04-21T18:00:00Z"
}

If there is also a fraud hold reason, the agent can call another approved function:

{
  "function_name": "get_fraud_hold_reason",
  "arguments": {
    "transfer_id": "TRF-102938"
  }
}

Now the final response can be precise:

“Transfer TRF-102938 is still pending because it’s waiting on bank processing. Estimated release time is 18:00 UTC today. There is no fraud hold on this transfer.”

That is materially better than generic chatbot output. The agent used real system data, stayed inside approved actions, and gave support staff or customers an answer they can trust.

For engineering managers, this also changes team design:

• •Product defines which actions are safe to expose.
• •Backend engineers implement those functions as stable APIs.
• •Security reviews permissions and input validation.
• •ML/AI engineers tune prompts and tool selection behavior.

That separation keeps ownership clear.

Related Concepts

• •

  Tool use
  Broader term for letting models interact with external systems. Function calling is one implementation pattern.

• •

  Agent orchestration
  The logic that decides when to call tools, in what order, and how to combine results.

• •

  Structured outputs
  Returning JSON or schema-constrained data so downstream systems can parse responses reliably.

• •

  Guardrails
  Rules that restrict what an agent can do, especially important for payments, refunds, disputes, and PII handling.

• •

  Human-in-the-loop approvals
  Requiring review before sensitive actions like reversing transactions or escalating fraud cases.

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.