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

ReAct is a pattern for AI agents 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 search, databases, APIs, and calculators instead of trying to answer everything from memory.

How It Works

The simplest way to understand ReAct is as a decision loop.

A normal chatbot gets a prompt and produces an answer. A ReAct agent does something closer to how a good operations analyst works:

• •It looks at the request
• •It decides what information it still needs
• •It takes one concrete step to get that information
• •It checks the result
• •It repeats until it can make a final decision

Think of it like a banker handling a loan exception case. The banker does not guess based on the first document they see. They review the application, check credit history, verify income, inspect policy exceptions, and only then make a recommendation.

That is ReAct in software form.

The loop usually looks like this:

1. •

   Reason

   • •The agent interprets the task.
   • •It identifies missing data or uncertainty.
2. •

   Act

   • •The agent calls a tool.
   • •Examples: query customer records, fetch policy details, calculate exposure, search internal docs.
3. •

   Observe

   • •The agent reads the tool output.
   • •It updates its understanding of the problem.
4. •

   Repeat

   • •If needed, it plans the next action.
   • •If enough evidence exists, it returns an answer or decision.

Here is the important part for fintech teams: ReAct is not just “chat with tools.” It is a controlled pattern for making agents behave more like operators than autocomplete.

For engineers, that means the model’s output is not only text. It becomes structured intermediate behavior:

• •thought
• •action
• •observation
• •final response

That structure matters because it makes agents more auditable and more useful in workflows where accuracy beats creativity.

Why It Matters

CTOs in fintech should care because ReAct maps well to real operational constraints.

• •

  It reduces blind answers

  • •Financial workflows need evidence.
  • •A ReAct agent can query systems before responding instead of hallucinating details from training data.
• •

  It fits exception handling

  • •Most valuable automation is not in happy-path flows.
  • •ReAct works well when cases require multiple checks across KYC, fraud signals, transaction history, or policy rules.
• •

  It improves tool use

  • •Fintech already has APIs for core banking, CRM, underwriting, risk scoring, and document retrieval.
  • •ReAct gives the model a disciplined way to use those tools in sequence.
• •

  It supports auditability

  • •You can log each action and observation.
  • •That helps with internal review, compliance investigations, and post-incident analysis.
• •

  It lowers product risk

  • •Instead of asking an LLM to “decide,” you constrain it to reason through steps.
  • •That makes it easier to insert approval gates and policy checks where needed.

Real Example

Let’s say your bank has an internal assistant for card dispute resolution.

A customer claims a debit card transaction was unauthorized. The support team wants the agent to triage the case before handing it to an investigator.

A ReAct flow could look like this:

1. •

   Reason

   • •The agent sees the claim amount, merchant name, timestamp, and customer profile.
   • •It notices missing context: recent card-present activity, velocity patterns, prior disputes.
2. •

   Act

   • •Query transaction history for the last 30 days.
   • •Check whether the card was present at nearby ATM withdrawals.
   • •Pull device fingerprinting data from mobile banking sessions.
   • •Retrieve fraud score and prior dispute count.
3. •

   Observe

   • •The transaction history shows three legitimate purchases before the disputed one.
   • •Device data shows login from a new device two hours earlier.
   • •Fraud score is elevated but not decisive.
   • •Prior disputes are zero.
4. •

   Reason again

   • •The agent concludes this is likely worth escalation but not enough for automatic chargeback approval.
   • •It identifies one more useful check: whether the merchant has been associated with prior fraud cases.
5. •

   Act

   • •Query merchant risk intelligence.
6. •

   Observe

   • •Merchant risk is moderate; no direct blacklist hit.
7. •

   Final response

   • •The agent summarizes findings for the investigator:
     • •disputed transaction details
     • •supporting signals
     • •recommended next step
     • •confidence level

This is useful because the agent did not just say “likely fraud.” It gathered evidence from multiple systems and produced a traceable recommendation.

For engineering teams, this pattern usually sits behind orchestration logic:

• •tool registry
• •policy engine
• •step limits
• •timeout handling
• •human approval thresholds

In production fintech systems, you do not want unlimited looping or free-form tool access. You want bounded reasoning with explicit actions and logged observations.

Related Concepts

• •

  Tool use / function calling

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

  Agent orchestration

  • •The control layer that manages steps, retries, approvals, and termination conditions.
• •

  Retrieval-Augmented Generation (RAG)

  • •Useful when agents need grounded context from documents or knowledge bases before acting.
• •

  Planning vs execution

  • •Planning decides what should happen; execution performs concrete system actions.
• •

  Human-in-the-loop workflows

  • •Essential in regulated environments where some decisions must be reviewed by staff before release.

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.