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

ReAct is an AI agent pattern that combines Reasoning and Acting in a loop: the model thinks about what to do next, takes an action, observes the result, then reasons again. In practice, it lets an agent solve multi-step tasks by alternating between internal planning and external tool use instead of trying to answer everything in one shot.

How It Works

Think of ReAct like a payments ops manager handling a failed card transaction.

You do not guess the root cause from memory alone. You check the auth response, look at the fraud decision, inspect issuer behavior, then decide whether to retry, escalate, or stop. ReAct works the same way: the model does not just “answer,” it moves through a loop.

The basic flow is:

• •Reason: The agent decides what it needs next.
• •Act: It calls a tool, queries a database, searches a policy doc, or hits an API.
• •Observe: It reads the result.
• •Repeat: It updates its plan and continues until it can respond.

A simple example:

1. •User asks: “Why did this payment fail?”
2. •Agent reasons: “I need the transaction record.”
3. •Agent acts: Calls the payments ledger API.
4. •Agent observes: Sees issuer_decline_code=51.
5. •Agent reasons again: “This is insufficient funds; check if retry is allowed.”
6. •Agent acts: Looks up retry policy.
7. •Agent responds: “Decline code 51 usually means insufficient funds. Retry is not recommended unless your retry policy allows delayed re-attempts.”

That loop is the point. ReAct is useful when the answer depends on facts outside the model’s weights.

For engineering managers, the important distinction is this:

• •A normal LLM response is a single pass.
• •A ReAct agent is a controlled workflow with decision points and tool calls.

That makes it easier to build systems that can inspect payment status, reconcile records, verify policy rules, and produce grounded answers.

Why It Matters

Engineering managers in payments should care because ReAct changes how you build reliable agentic systems.

• •

  Better grounding on real data

  • •Payments work depends on live state: auth results, settlement status, chargeback windows, KYC flags.
  • •ReAct lets the agent fetch those facts before answering.
• •

  Lower hallucination risk

  • •If an agent must explain why a payment failed or whether a refund is eligible, guessing is not acceptable.
  • •Tool-backed reasoning reduces unsupported answers.
• •

  Fits operational workflows

  • •Payments teams already work in loops: investigate, verify, act, confirm.
  • •ReAct maps naturally to dispute handling, reconciliation checks, merchant support triage, and fraud review.
• •

  Easier to audit

  • •Each step can be logged: what the agent thought it needed, what tool it used, and what it observed.
  • •That matters for incident reviews and compliance conversations.

Here’s a quick comparison:

For payments specifically, ReAct helps when inputs are incomplete or distributed across systems. That includes PSP dashboards, ledger services, fraud engines, CRM notes, and policy docs.

Real Example

Imagine a bank’s support team gets this ticket:

“My card payment was declined twice at an online merchant. Was it fraud or something else?”

A ReAct agent can handle this like an experienced analyst:

1. •

   Reason

   • •Determine which data sources are needed:
     • •transaction authorization log
     • •fraud engine decision
     • •merchant category
     • •cardholder risk profile
     • •retry history
2. •

   Act

   • •Query the auth service for both attempts.
   • •Check fraud platform output.
   • •Look up whether the merchant was flagged as high risk.
3. •

   Observe

   • •First attempt: decline_code=05
   • •Fraud engine: step_up_required=true
   • •Second attempt after OTP challenge: approved
   • •Merchant not blocked
4. •

   Reason again

   • •Decline code 05 usually means generic issuer decline or soft decline depending on network context.
   • •The step-up requirement suggests risk controls triggered rather than a hard account problem.
5. •

   Respond

   • •“This looks like a risk-based decline on the first attempt. The second payment was approved after additional verification, so this was likely fraud screening rather than a permanent card issue.”

That response is better than a raw LLM guess because it is tied to actual system state.

For an engineering manager in payments, this matters because you can route these agents into real workflows:

• •Tier-1 support triage
• •Dispute intake
• •Merchant onboarding review
• •Refund eligibility checks
• •Reconciliation exception handling

The implementation detail that matters most is control. You do not want an unconstrained agent calling random tools. You want:

• •allowed tools only
• •scoped permissions
• •deterministic fallbacks
• •full trace logging
• •human review for edge cases

Related Concepts

A few adjacent topics you should know:

• •

  Tool calling

  • •The mechanism that lets an LLM invoke APIs or functions.
  • •ReAct often uses tool calling as its action layer.
• •

  Chain-of-thought prompting

  • •Internal reasoning style used to improve step-by-step problem solving.
  • •ReAct adds external actions on top of that reasoning loop.
• •

  Agent orchestration

  • •Managing multi-step workflows across tools, policies, and retries.
  • •Useful when you need more than one model call per task.
• •

  Function routing / tool selection

  • •Choosing which service to call based on user intent and state.
  • •Critical in payments where misuse of tools can create operational risk.
• •

  Observability for agents

  • •Logging prompts, tool calls, outputs, latency, and failures.
  • •Non-negotiable if you want to run agents in regulated environments.

If you are building AI agents in payments, treat ReAct as a control pattern first and an LLM pattern second. The model does not replace your systems; it coordinates them with better context than static automation can provide.

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.