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

ReAct is a pattern for AI agents that combines Reasoning and Acting in a loop. In practice, the model thinks about the task, takes an action like calling a tool or querying a system, observes the result, then reasons again before deciding the next step.

How It Works

Think of ReAct like a loan officer working an application file with a checklist.

They do not stare at the file and guess the answer. They inspect one document, check a rule, call another system if needed, then update their decision based on what they found.

That is the core of ReAct:

• •Reason: decide what to do next
• •Act: use a tool, API, database, or workflow step
• •Observe: read the result
• •Repeat: continue until the task is done

For lending systems, this matters because most useful agent work is not pure chat. It is multi-step work across systems:

• •Pull borrower data from a CRM
• •Check income verification status
• •Query bureau data
• •Validate policy rules
• •Escalate exceptions to an underwriter

ReAct gives the agent a structure for doing that without pretending it knows everything up front.

A simple analogy: imagine a mortgage processor reviewing a borderline application.

Without this loop, an agent either:

• •guesses too early, or
• •dumps all available tools into one giant workflow and becomes hard to control

ReAct sits in the middle. It keeps the model grounded in evidence while still letting it adapt as new information appears.

Why It Matters

Developers in lending should care because ReAct maps well to real operational work.

• •

  It reduces blind answers

  Lending workflows depend on facts: income, DTI, LTV, fraud flags, policy exceptions. ReAct forces the agent to fetch evidence before responding.

• •

  It fits messy real-world cases

  Not every application follows one path. If a document is missing or a rule conflicts with another rule, the agent can inspect the issue and choose the next step dynamically.

• •

  It makes tool use explicit

  Instead of hiding logic inside prompts, ReAct shows when the agent queried LOS data, pulled bureau info, or checked underwriting policy. That is easier to audit.

• •

  It works better with human review

  Lending often needs escalation. A ReAct agent can gather context first, then hand off with a clean summary of what it checked and what remains unresolved.

Real Example

Say you are building an assistant for personal loan pre-screening.

A borrower asks: “Can I qualify for a $25k unsecured loan?”

A ReAct-style agent would not answer from memory. It would move through steps like this:

1. •

   Reason

   • •Determine required checks: income verification, existing debt load, credit score threshold, employment status
2. •

   Act

   • •Call get_customer_profile(customer_id)
   • •Call fetch_credit_bureau_report(ssn_hash)
   • •Call get_income_verification_status(application_id)
3. •

   Observe

   • •Profile shows self-employed borrower
   • •Bureau report shows score 684
   • •Income verification is stale and older than policy allows
4. •

   Reason

   • •Since income verification is stale, check whether bank statement aggregation exists
5. •

   Act

   • •Call get_bank_statement_link_status(application_id)
6. •

   Observe

   • •No linked bank statements found
7. •

   Reason

   • •The agent should not approve or deny.
   • •It should summarize missing requirements and route to manual review or ask for updated documents

The final response might be:

“Based on current data, I can’t confirm eligibility yet. Credit score meets minimum policy, but income verification is expired and bank statements are missing. Please upload updated proof of income or route this file to underwriting review.”

That is useful because it is:

• •grounded in actual system state
• •explainable to ops teams
• •safer than hallucinating an approval path

For engineering teams, the implementation usually looks like an orchestration loop around an LLM plus tools:

while not done:
    thought = llm.generate(context)
    action = parse_action(thought)

    if action.name == "get_credit_report":
        observation = credit_api.fetch(action.args["customer_id"])
    elif action.name == "check_policy":
        observation = policy_engine.evaluate(action.args)
    else:
        observation = {"error": "unknown_action"}

    context.append({"action": action, "observation": observation})

In production, you would add:

• •strict tool schemas
• •timeouts and retries
• •audit logs for every action
• •guardrails for PII access
• •deterministic policy checks outside the model when possible

That last point matters in lending: ReAct helps with orchestration and reasoning over incomplete information, but final eligibility decisions should still rely on deterministic rules where compliance requires it.

Related Concepts

• •

  Tool calling

  The mechanism that lets an LLM invoke APIs or functions during execution.

• •

  Chain-of-thought prompting

  A reasoning style where the model breaks down problems step by step; ReAct extends this by adding actions and observations.

• •

  Function orchestration

  Coordinating multiple services like LOS, CRM, bureau providers, and document stores in one workflow.

• •

  Agentic workflows

  Broader systems where an AI agent plans tasks and executes them across tools with limited supervision.

• •

  Policy engines

  Deterministic rule systems that should handle hard compliance logic instead of relying on model judgment alone.

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.