What is ReAct in AI Agents? A Guide for CTOs in payments

ReAct is an AI agent pattern that combines Reasoning and Acting in a loop: the model thinks about what to do, takes an action, observes the result, then thinks again. In practice, ReAct lets an agent use tools like APIs, databases, and search instead of trying to solve everything from memory in one shot.

How It Works

Think of ReAct like a senior payments ops analyst handling a disputed transaction.

They do not stare at the case file and guess. They inspect the ledger, check the card network response, query the fraud system, look at the merchant profile, then decide the next step. Each new fact changes their reasoning.

That is ReAct.

The loop is simple:

• •Reason: decide what information is missing
• •Act: call a tool or API
• •Observe: read the result
• •Repeat: update the plan based on what came back

For a payments CTO, the important part is this: ReAct turns an LLM from a text generator into a control layer that can operate across systems. It can ask for transaction history, check risk signals, retrieve policy rules, and then produce an answer grounded in actual data.

A basic flow looks like this:

1. •User asks: “Why was this payment declined?”
2. •Agent reasons: “I need auth response code, fraud score, issuer response, and recent account activity.”
3. •Agent acts:
   • •calls get_transaction(txn_id)
   • •calls get_fraud_score(customer_id)
   • •calls get_decline_reason(txn_id)
4. •Agent observes:
   • •decline code = 05
   • •fraud score = high
   • •velocity rule triggered
5. •Agent reasons again: “This was likely blocked by risk controls, not issuer funds.”
6. •Agent responds with a grounded explanation.

The key difference from a plain chatbot is that ReAct does not rely on one static prompt and one static answer. It uses intermediate steps to gather evidence before deciding.

Why It Matters

CTOs in payments should care because ReAct maps well to real operational workflows.

• •

  Better answers for complex cases

  • •Payments issues are rarely single-hop questions.
  • •A chargeback, payout failure, or settlement mismatch usually requires checking multiple systems.
• •

  Lower hallucination risk

  • •The model is forced to fetch data before answering.
  • •That matters when you are explaining declines, fees, disputes, or compliance outcomes.
• •

  Works with existing enterprise systems

  • •ReAct fits APIs you already have: ledger services, KYC/AML systems, fraud engines, CRM tools.
  • •You do not need to replace core infrastructure to use it.
• •

  More auditable behavior

  • •Each tool call becomes part of the trace.
  • •That gives engineering and risk teams something concrete to review when an agent makes a decision.
• •

  Useful for human-in-the-loop operations

  • •In payments, many workflows need escalation rather than full automation.
  • •ReAct can gather context first and then hand off a structured summary to ops or compliance.

Real Example

Imagine a bank support workflow for failed card-not-present transactions.

A merchant reports that several legitimate customer payments were declined overnight. The support team needs to know whether this was issuer behavior, internal fraud controls, or a configuration issue.

A ReAct-based agent could handle the investigation like this:

1. •

   Reason

   • •“I need to identify whether declines came from issuer responses or bank-side rules.”
2. •

   Act

   • •Call list_failed_authorizations(merchant_id, time_window)
   • •Call get_decline_codes(batch_of_txns)
   • •Call get_fraud_rule_hits(merchant_id)
   • •Call check_processor_status(region)
3. •

   Observe

   • •Most transactions returned decline code 05
   • •Fraud rule velocity_threshold fired on three customer clusters
   • •Processor status shows no outage
   • •Merchant recently changed checkout flow and retried authorizations aggressively
4. •

   Reason

   • •“This looks like internal risk logic plus retry behavior, not a processor incident.”
5. •

   Respond

   • •The agent produces:
     • •summary of impacted transactions
     • •likely root cause
     • •evidence from logs and rules
     • •recommended next action: tune velocity thresholds or adjust retry policy

This is where ReAct earns its keep. Without it, the model would either guess or produce generic support text. With it, you get an investigation workflow that mirrors how experienced payments engineers work.

For regulated environments, that trace matters as much as the answer itself. If your team needs to explain why an authorization was blocked or why a refund stalled, you want each step visible:

Related Concepts

• •

  Tool use / function calling

  • •The mechanism that lets an LLM call APIs instead of only generating text.
• •

  Planning agents

  • •Agents that create multi-step plans before execution; often used alongside ReAct.
• •

  RAG (Retrieval-Augmented Generation)

  • •Pulls relevant documents into context; useful for policies, runbooks, and scheme rules.
• •

  Workflow orchestration

  • •Deterministic process control using engines like Temporal or Step Functions.
  • •Useful when you need strict state handling around payment operations.
• •

  Human-in-the-loop review

  • •Keeps sensitive actions under operator control.
  • •Common in disputes, AML reviews, refund approvals, and exception handling.

If you are building AI agents for payments, treat ReAct as a practical pattern for turning language models into investigators and operators. It is not magic; it is disciplined interaction between reasoning and tools. That discipline is what makes it useful in financial systems where accuracy beats cleverness every time.

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.