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

ReAct is an AI agent pattern that combines Reasoning and Acting in a loop: the model thinks about the task, takes an action, observes the result, then thinks again. In practice, ReAct lets an agent decide what to do next based on live feedback instead of trying to answer everything in one shot.

How It Works

Think of ReAct like a senior banker handling a complex customer request at a branch.

They do not guess the answer immediately. They check the account system, review policy notes, maybe call another department, then use what they learned to decide the next step.

That is ReAct:

• •Reasoning: the agent plans its next move
• •Action: it calls a tool, API, database, or workflow
• •Observation: it reads the result
• •Repeat: it updates its plan and continues

For banking teams, this matters because most useful agent tasks are not pure chat. They require interaction with systems:

• •customer profile lookup
• •transaction history checks
• •KYC/AML policy validation
• •fraud score retrieval
• •case management updates

A simple prompt-response model can only produce text. A ReAct agent can work through a task like:

1. •“Check whether this transfer is allowed.”
2. •Query customer limits.
3. •Inspect recent activity.
4. •Compare against policy.
5. •Decide whether to approve, escalate, or ask for more data.

The key difference is that ReAct keeps the model grounded in evidence from tools rather than letting it improvise from memory.

Here is the basic flow:

User request -> Reason -> Tool call -> Observe result -> Reason again -> Final answer/action

In implementation terms, you usually give the agent:

• •a system instruction describing its role and constraints
• •access to approved tools only
• •a loop controller that stops after success or max iterations
• •logging for every thought/action/observation step

That last part matters in regulated environments. If a model touches customer-facing workflows, you need traceability for audit, incident review, and model risk management.

Why It Matters

CTOs in banking should care because ReAct solves real operational problems that standard LLM chat does not.

• •

  It reduces hallucinations

  • •The agent can verify facts against internal systems before answering.
  • •That is critical when dealing with balances, eligibility rules, or compliance checks.
• •

  It enables real workflows

  • •A ReAct agent can do more than draft text.
  • •It can gather data, route cases, trigger alerts, and update CRM or ticketing systems.
• •

  It supports controlled autonomy

  • •You can constrain which tools are available and when escalation is required.
  • •That gives you a safer path than fully autonomous agents.
• •

  It improves auditability

  • •Each action and observation can be logged.
  • •For banks, that makes review by risk, compliance, and internal audit much easier.

There is also a practical architecture benefit: ReAct works well when your data is fragmented across core banking systems, policy engines, and case tools. Instead of trying to centralize everything first, you let the agent orchestrate access under strict controls.

Real Example

Consider a retail banking scenario: a customer asks why their international transfer was delayed.

A non-agentic chatbot might respond with generic language:

“International transfers may take 1–3 business days depending on intermediary banks.”

That is not enough for support or operations.

A ReAct-based agent can handle it properly:

1. •

   Reason

   • •Determine whether this is an informational request or an exception case.
   • •Check if the transfer reference number is present.
2. •

   Act

   • •Call the payments API to fetch transfer status.
   • •Query sanctions screening results.
   • •Check whether any manual compliance review was triggered.
3. •

   Observe

   • •Status shows “pending review.”
   • •Screening returned no hit.
   • •The payment exceeded an internal threshold requiring approval.
4. •

   Reason

   • •Conclude that the delay is due to internal approval routing, not network failure.
   • •Decide whether to provide an explanation or escalate to operations.
5. •

   Act

   • •Draft a customer-safe response.
   • •Create a case note for the operations queue if SLA is breached.

Example output:

“Your transfer is pending internal approval because it exceeded our review threshold. There are no sanctions screening issues on record. I’ve opened a case with operations and will notify you once it moves forward.”

This is where ReAct becomes valuable in banking: it turns an LLM into an orchestrator that can inspect state before speaking.

A simple architecture for this looks like:

For insurance teams, the same pattern applies to claims triage:

• •inspect policy coverage
• •check claim history
• •request missing documents
• •escalate suspicious cases

The pattern stays the same even if the domain changes.

Related Concepts

• •

  Tool calling

  • •The mechanism that lets models invoke APIs or functions directly.
• •

  Function orchestration

  • •Managing multi-step workflows across systems with rules and retries.
• •

  Agent memory

  • •Storing state across steps so the agent does not lose context mid-task.
• •

  RAG (Retrieval-Augmented Generation)

  • •Pulling grounded information from documents or knowledge bases before answering.
• •

  Guardrails and policy engines

  • •Hard controls that limit what an agent can say or do in regulated environments.

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.