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

ReAct is an AI agent pattern that combines Reasoning and Acting in a loop, so the model can think about a task, take an action with a tool, observe the result, and then decide the next step. ReAct stands for Reason + Act, and it is used to make agents more reliable when they need to work with external systems like APIs, databases, and policy platforms.

How It Works

Think of ReAct like a claims adjuster working a case.

They do not stare at the file and guess the outcome. They check the policy, inspect the claim notes, ask for missing documents, review photos, then decide what to do next. That is the ReAct loop: think, act, observe, repeat.

In an AI agent, the flow usually looks like this:

• •

  Reasoning

  • •The model interprets the user request.
  • •It decides what information is missing.
  • •It plans the next best action.
• •

  Action

  • •The agent calls a tool.
  • •Examples: policy lookup API, CRM search, claims system query, document parser.
• •

  Observation

  • •The tool returns data.
  • •The agent reads the result and updates its plan.
• •

  Next step

  • •The agent either asks for more input, takes another action, or produces an answer.

This matters because insurance workflows are rarely single-step. A customer asks: “Is this water damage covered?” The answer depends on policy type, exclusions, deductible, claim history, jurisdiction, and sometimes endorsements. ReAct gives the agent a way to gather those facts instead of guessing from one prompt.

A simple mental model:

For developers, the key detail is that ReAct is not just “chat with tools.” It is a control loop. The model decides when to stop reasoning and when to call something real.

A minimal flow might look like this:

User: "Can this claim be escalated?"
Agent thinks: I need policy status and claim age.
Agent acts: call claims API
Agent observes: claim opened 18 days ago; escalation threshold is 14 days
Agent acts: call policy rules service
Agent observes: escalation allowed
Agent responds: yes, escalate with reason code X

That loop is why ReAct works well in insurance operations where decisions depend on live system state.

Why It Matters

• •

  Reduces bad answers

  • •Insurance agents need current facts from policy admin systems and claims platforms.
  • •ReAct forces the model to verify before responding.
• •

  Fits regulated workflows

  • •Underwriting support, FNOL triage, and claims servicing often require traceable steps.
  • •Each action in the loop can be logged for audit and review.
• •

  Improves tool use

  • •Instead of stuffing every rule into prompts, you let the agent query source systems.
  • •That keeps prompts smaller and logic closer to systems of record.
• •

  Handles multi-step tasks better

  • •Many insurance requests need branching logic.
  • •Example: check coverage → inspect exclusions → confirm deductible → decide next action.

For engineering teams, this means less brittle prompt engineering and more structured orchestration. You are building an agent that can interrogate your systems instead of pretending it already knows them.

Real Example

Let’s say you are building an internal assistant for claims handlers at an insurer. A handler asks:

“Can we approve this wind damage claim under Policy #48219?”

A ReAct agent would handle it like this:

1. •

   Reason

   • •Identify required checks:
     • •active policy?
     • •peril covered?
     • •deductible amount?
     • •any open exclusions?
     • •prior claim count?
2. •

   Act

   • •Call get_policy(policy_id)
   • •Call get_claim(claim_id)
   • •Call get_coverage_rules(policy_type)
3. •

   Observe

   • •Policy active: yes
   • •Wind damage covered: yes
   • •Deductible: $1,000
   • •Exclusion found: roof maintenance exclusion if damage caused by wear-and-tear
   • •Claim notes show storm event date matches weather report
4. •

   Reason again

   • •Coverage applies unless exclusion evidence exists.
   • •Need one more check on inspection notes for wear-and-tear indicators.
5. •

   Act again

   • •Call get_inspection_report(claim_id)
6. •

   Observe

   • •Report shows storm-related shingle loss; no wear-and-tear flags
7. •

   Answer

   • •Recommend approval subject to deductible
   • •Include rationale and source references

The important part is not just the final answer. It is that each step can be validated against system data.

Here is a simplified orchestration example:

def react_claim_decision(agent_state):
    thought = llm.plan(agent_state)

    if thought.needs_policy_lookup:
        policy = get_policy(agent_state.policy_id)
        agent_state.add_observation(policy)

    if thought.needs_claim_lookup:
        claim = get_claim(agent_state.claim_id)
        agent_state.add_observation(claim)

    if thought.needs_more_evidence:
        report = get_inspection_report(agent_state.claim_id)
        agent_state.add_observation(report)

    return llm.finalize(agent_state)

In production you would add:

• •tool timeouts
• •retries
• •schema validation
• •guardrails on allowed actions
• •audit logging of every observation

That is where ReAct becomes useful in insurance-grade systems rather than just demos.

Related Concepts

• •

  Tool calling / function calling

  • •The mechanism that lets an LLM invoke APIs or services.
• •

  Chain-of-thought planning

  • •Internal reasoning used to decide what to do next.
  • •In production systems you often keep this hidden but still use the structure.
• •

  RAG (Retrieval-Augmented Generation)

  • •Pulls documents or knowledge base entries into context before answering.
  • •Useful for policy wording and underwriting guidelines.
• •

  Agent orchestration

  • •Managing state, steps, retries, memory, and tool execution across a workflow.
• •

  Guardrails / policy enforcement

  • •Rules that restrict what the agent can do and how it should respond 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.