What is tool use in AI Agents? A Guide for compliance officers in payments

Tool use in AI agents is the ability for an agent to call external systems or functions to get information, take action, or complete a task. In payments, that means the agent can do things like check transaction status, query sanctions screening results, pull policy rules, or open a case in a workflow system instead of only generating text.

How It Works

Think of an AI agent like a compliance analyst with access to a desk full of approved tools.

The model itself is the decision-maker. The tools are the systems it is allowed to use:

• •Transaction monitoring platform
• •Sanctions screening engine
• •KYC/AML case management system
• •Payment gateway API
• •Internal policy database

The agent does not “know” the answer in advance. It decides it needs more context, selects the right tool, sends a structured request, receives the result, then uses that result to continue.

A simple flow looks like this:

1. •A user asks: “Can we release this payment?”
2. •The agent sees it needs more than language generation.
3. •It calls a tool like get_transaction_details(transaction_id).
4. •It gets back data such as amount, counterparty, corridor, and screening flags.
5. •It may then call check_sanctions_match(name, country) or fetch_case_history(case_id).
6. •It summarizes the result and recommends next steps.

For compliance teams, the important point is this: tool use turns an AI agent from a chatbot into a controlled operator inside your process.

A useful analogy is online banking with two-factor authentication.

The bank app does not just “think” about whether you are authorized. It checks identity through a separate trusted mechanism before allowing action. Tool use works similarly: the model proposes what to do, but approved systems verify facts and execute actions.

Why It Matters

Compliance officers in payments should care because tool use changes both capability and risk.

• •

  It improves accuracy

  • •The agent can retrieve current data instead of guessing from stale training patterns.
  • •That matters for sanctions lists, transaction status, customer risk ratings, and policy thresholds.
• •

  It creates auditability

  • •Every tool call can be logged: who asked, what was queried, what came back, and what action followed.
  • •That gives you evidence for reviews, audits, and incident investigations.
• •

  It introduces control points

  • •You can restrict which tools exist, which users can trigger them, and what fields they can access.
  • •This is where segregation of duties and least privilege become practical design choices.
• •

  It changes failure modes

  • •A bad answer is no longer just “wrong text.”
  • •A bad tool call could freeze a payment, open an unnecessary case, or expose restricted data if controls are weak.

Real Example

A payment operations team receives an alert on a cross-border transfer for $48,000 from a corporate client in the UAE to a beneficiary in Eastern Europe.

An AI agent is embedded in the case review workflow. The analyst asks: “Summarize why this payment was flagged and tell me whether it should stay on hold.”

The agent uses tools in sequence:

1. •

   get_alert_details(alert_id)

   • •Returns reason codes: unusual amount, new beneficiary, high-risk corridor.
2. •

   fetch_customer_profile(customer_id)

   • •Returns KYC tier, expected activity range, beneficial ownership notes.
3. •

   query_sanctions_screening(beneficiary_name)

   • •Returns no exact match but one fuzzy match requiring review.
4. •

   retrieve_policy_rule("cross_border_hold_threshold")

   • •Returns internal rule: hold if high-risk corridor plus unresolved screening match.
5. •

   create_case_note(summary)

   • •Writes a structured note into the case management system for human review.

The final output is not “approve” or “reject” based on guesswork. It says something like:

• •Alert triggered by corridor risk and new beneficiary
• •Screening produced one unresolved fuzzy match
• •Policy requires manual review before release
• •Recommended next step: keep hold until analyst clears match

That is tool use done properly: the agent gathers facts from approved systems, applies policy context, and supports—not replaces—the compliance decision.

Related Concepts

• •

  Function calling

  • •The technical pattern where an LLM invokes predefined functions with structured inputs.
• •

  Agent orchestration

  • •How multiple steps are chained together: decide, call tool, inspect result, act again.
• •

  Retrieval-Augmented Generation (RAG)

  • •Pulling documents or policy text into the model’s context before generating an answer.
  • •Different from tool use when the system performs actions or queries live systems.
• •

  Human-in-the-loop controls

  • •Requiring analyst approval before sensitive actions like releasing funds or closing cases.
• •

  Audit logging and traceability

  • •Recording every decision step so compliance can reconstruct what happened later.

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.