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

ReAct is an AI agent pattern that combines Reasoning and Acting in a loop, so the model can think through a task, take a tool action, observe the result, and then decide what to do next.
In practice, ReAct lets an agent solve problems by alternating between internal reasoning and external actions like searching a database, calling an API, or checking a ledger.

How It Works

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

They do not stare at one screen and guess. They check the transaction log, notice the merchant category, call another system to verify settlement status, inspect the chargeback reason code, then decide whether to escalate or auto-resolve.

That is the ReAct loop:

• •Reason: decide what information is missing
• •Act: call a tool or API
• •Observe: read the output
• •Repeat: refine the next step until the answer is good enough

For developers, the important part is that the model is not just generating text. It is orchestrating steps against real systems.

A typical flow looks like this:

The key difference from a plain chatbot is control flow. A normal LLM answers from its prompt and memory. A ReAct agent can pause, query systems of record, and use those observations to update its plan.

For payments teams, this matters because many workflows are data-dependent:

• •transaction investigation
• •refund eligibility checks
• •KYC/AML support triage
• •payout failure analysis
• •reconciliation exceptions

ReAct gives you a structured way to let the model work like an analyst with tools instead of like a static FAQ bot.

Why It Matters

Developers in payments should care because ReAct maps well to operational work where answers depend on live systems.

• •

  Better answers for stateful problems

  • •Payments issues usually require current status from multiple systems.
  • •ReAct lets the agent fetch what it needs instead of hallucinating from stale context.
• •

  Cleaner tool orchestration

  • •You can expose narrow APIs for ledger lookup, payment status, sanctions screening results, or dispute case history.
  • •The agent decides when to call each tool.
• •

  More auditable behavior

  • •Each reason-act-observe step can be logged.
  • •That helps with internal review, incident analysis, and regulated workflows.
• •

  Lower support load

  • •Many Tier 1 and Tier 2 cases are repetitive.
  • •ReAct agents can gather evidence before escalating to a human.

If you are building in payments, this pattern is useful anywhere the workflow depends on multiple systems and intermediate evidence. It is less useful for pure content generation and more useful for operational decision support.

Real Example

Let’s say a merchant support team gets this request:

“A customer says their card payment was charged twice. Is this a duplicate charge or just an authorization hold?”

A ReAct agent could handle it like this:

1. •

   Reason

   • •The agent identifies that it needs transaction history, authorization records, capture events, and settlement status.
   • •It also knows it should distinguish between:
     • •duplicate captures
     • •auth + capture confusion
     • •pending hold that later falls off
2. •

   Act

   • •Call getTransactionById(txn_123)
   • •Call listCardEvents(card_789, last_24h)
   • •Call getSettlementStatus(txn_123)
3. •

   Observe

   • •First event: authorization for $42.00
   • •Second event: another authorization for $42.00 after retry
   • •Only one capture exists
   • •One auth is still pending and not captured
4. •

   Reason again

   • •This looks like an authorization retry issue, not double charging.
   • •The customer may see two pending holds temporarily.
   • •The correct action is to explain that one hold should expire automatically unless both are captured later.
5. •

   Act again if needed

   • •Create a support note
   • •Trigger a customer-facing explanation template
   • •Escalate only if both authorizations become captured

That workflow is exactly why ReAct is useful in banking and payments operations. The model does not need to “know” everything upfront. It needs access to the right tools and a disciplined loop for deciding what to do next.

A simple implementation often looks like this:

while not done:
    thought = llm.plan(context)
    action = select_tool(thought)

    result = tools[action.name](**action.args)
    context.append({
        "thought": thought,
        "action": action.name,
        "observation": result,
    })

    done = llm.is_complete(context)

In production, you would add guardrails:

• •allowlisted tools only
• •strict schemas for inputs/outputs
• •retries with idempotency keys for external calls
• •audit logs for every tool invocation
• •human approval for money-moving actions

That last point matters. In payments, ReAct should help investigate and recommend. It should not autonomously move funds unless your controls are designed for that level of automation.

Related Concepts

If you are learning ReAct, these adjacent topics are worth understanding next:

• •Tool calling
  • •How an LLM invokes APIs with structured arguments.
• •Function calling
  • •A common implementation style for exposing backend capabilities to agents.
• •Chain-of-thought planning
  • •The reasoning side of agent behavior; useful conceptually even when hidden in production.
• •Agent memory
  • •Short-term context vs long-term state across sessions or cases.
• •Human-in-the-loop workflows
  • •Approval steps for sensitive actions like refunds, disputes, or account changes.

ReAct is not magic. It is a practical pattern for making agents useful in environments where correctness depends on real systems, not just generated text. For payments teams, that makes it one of the first agent patterns worth learning well.

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.