What is ReAct in AI Agents? A Guide for product managers in payments

ReAct is an AI agent pattern that combines reasoning and acting in a loop. The model thinks about the next step, takes an action like calling a tool or querying data, then uses the result to decide what to do next.

In payments, that means an agent can inspect a failed transaction, decide whether to check fraud rules, call a ledger API, or ask for more context, instead of producing one static answer.

How It Works

ReAct stands for Reason + Act.

A normal chatbot answers from memory. A ReAct agent behaves more like a good payments ops analyst: it looks at the case, decides what evidence it needs, pulls that evidence, then updates its conclusion.

Think of it like handling a card payment dispute at a call center:

• •The agent sees the complaint: “Customer says they were charged twice.”
• •It reasons: “I need to verify if this is a duplicate authorization or two settled captures.”
• •It acts: queries the payment gateway and ledger.
• •It sees the results.
• •It reasons again: “These are two separate authorizations, but only one settled. This is likely a pending reversal issue.”
• •It acts again if needed: checks refund status or creates a case note.

That loop is the core idea.

For product managers, the important part is this: ReAct is not one big model response. It is a controlled workflow where the model can pause, inspect tools and data, then continue. That makes it much better for tasks that depend on live systems.

Simple mental model

If you want one analogy: ReAct is like a claims investigator with access to internal systems. They do not guess first and write a report later. They gather facts as they go.

Why It Matters

• •

  Better handling of ambiguous cases

  • •Payments issues are rarely clean.
  • •A ReAct agent can inspect multiple signals before deciding whether something is fraud, routing failure, customer error, or settlement lag.
• •

  Fewer blind guesses

  • •Without tool use, an LLM may sound confident while being wrong.
  • •ReAct forces the agent to check real systems before answering.
• •

  More useful automation

  • •Product teams often want “AI support” that does more than draft text.
  • •ReAct enables actions like checking transaction status, pulling KYC data, creating tickets, or suggesting next-best actions.
• •

  Easier to control than free-form generation

  • •You can define which tools are available and when they can be used.
  • •That matters in regulated environments where every action needs traceability.

Real Example

Let’s say you run payments operations for a digital bank.

A customer reports: “My debit card was charged twice for one merchant purchase.”

A basic chatbot might respond:

“Please wait 3–5 business days while the charge settles.”

A ReAct agent can do better because it can investigate.

What the agent does

1. •

   Reason

   • •Determine whether this is:
     • •two authorizations,
     • •one authorization plus one capture,
     • •or an actual duplicate charge.
2. •

   Act

   • •Call the card processor API to fetch authorization events.
   • •Call the ledger service to check settlement records.
   • •Check refund and reversal history.
3. •

   Observe

   • •Finds:
     • •two auth holds for $42 each,
     • •only one settled capture,
     • •one pending reversal on the second hold.
4. •

   Reason again

   • •Concludes this is not double billing.
   • •The customer likely sees both pending entries in their banking app.
5. •

   Act

   • •Drafts a support response.
   • •Optionally creates an internal note explaining when the pending hold should disappear.

Why this helps

The customer gets a precise answer instead of generic advice. The support team gets fewer escalations. The product team gets better visibility into where confusion comes from: processor behavior, app display logic, or settlement timing.

Here’s what that looks like in simplified form:

User issue -> Agent reasons about likely cause
          -> Agent checks payment processor
          -> Agent checks ledger/settlement
          -> Agent decides whether it is duplicate billing
          -> Agent responds or escalates

For engineering teams building this pattern, the value is not just “LLM intelligence.” The value is that reasoning stays tied to system evidence. That reduces hallucination risk and makes audit trails easier to build.

Related Concepts

• •

  Tool calling

  • •The mechanism that lets an agent query APIs, databases, or internal services during execution.
• •

  Function calling

  • •Similar to tool calling; often used when models trigger structured backend functions with typed inputs and outputs.
• •

  Agent orchestration

  • •The control layer that decides which tools exist, in what order they run, and when to stop looping.
• •

  Chain-of-thought vs ReAct

  • •Chain-of-thought is internal reasoning.
  • •ReAct adds external actions and observations between reasoning steps.
• •

  Workflow automation

  • •Traditional deterministic flows still matter.
  • •ReAct sits on top when decisions require live context or flexible investigation.

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.