What is ReAct in AI Agents? A Guide for compliance officers in insurance

ReAct is an AI agent pattern that combines Reasoning and Acting in a loop: the model thinks about the task, takes a step, observes the result, then decides what to do next. ReAct stands for Reason + Act, and it lets an agent use tools, APIs, or systems instead of trying to answer everything from memory.

How It Works

Think of ReAct like a compliance officer reviewing a suspicious claim file.

You do not make one giant decision from the first document you see. You:

• •review the file
• •notice missing evidence
• •request more records
• •inspect the new information
• •decide whether to escalate

That is the ReAct loop.

In an AI agent, the pattern looks like this:

1. •

   Reason
   The agent interprets the task and decides what information it needs.

2. •

   Act
   It uses a tool: search policy documents, query a claims system, check a sanctions list, or call a workflow API.

3. •

   Observe
   It reads the tool output.

4. •

   Repeat
   It reasons again with the new evidence until it can produce an answer or take the next action.

A simple version looks like this:

User: "Check whether this claim needs manual review."

Agent thinks: I need policy coverage details and claim history.
Agent acts: Query policy admin system.
Agent observes: Coverage active; flood exclusion present.
Agent thinks: I need claim cause and location.
Agent acts: Query claims intake record.
Agent observes: Water damage after storm surge in coastal zone.
Agent concludes: Route to manual review due to possible exclusion and high-risk geography.

For compliance teams, the important point is that ReAct is not just “chat with tools.” It is a structured decision loop. That makes it easier to design controls around what the agent can access, when it can act, and how its decisions are logged.

Why It Matters

• •

  Better control over automated decisions
  ReAct agents can be constrained to specific tools and workflows. That matters when you need separation between informational lookups and actual business actions.

• •

  More auditable than free-form prompting
  Each step in the loop can be logged: what the agent asked for, what it saw, and why it moved forward. That gives compliance teams something closer to an evidence trail.

• •

  Supports human-in-the-loop escalation
  You can force the agent to stop after certain observations and route cases to reviewers. That is useful for claims exceptions, suspicious activity checks, and adverse decision support.

• •

  Reduces hallucination risk when paired with tools
  Instead of guessing policy terms or regulatory rules from memory, the agent can retrieve source data before answering. That lowers risk, but only if tool access is tightly governed.

Real Example

Imagine an insurance carrier using an AI agent to triage incoming property claims after a major storm.

The business goal is simple: identify claims that may need manual review because of flood exclusions or fraud indicators.

Here is how ReAct works in practice:

1. •The adjuster submits a claim summary.
2. •The agent reasons that it needs:
   • •policy coverage details
   • •loss location
   • •cause of damage
   • •prior claims history
3. •The agent acts by querying approved internal systems:
   • •policy administration platform
   • •claims management system
   • •geolocation service
4. •The agent observes:
   • •active homeowners policy
   • •flood exclusion clause present
   • •loss location in a coastal flood zone
   • •prior water-damage claim within 12 months
5. •The agent reasons again:
   • •this does not prove fraud
   • •but it does create a coverage exception risk and warrants review
6. •The agent outputs:
   • •“Route to manual review”
   • •supporting facts used
   • •source systems queried

That workflow is useful because it keeps the model from making a final coverage determination on its own. It gathers evidence first, then recommends an action based on policy data and documented rules.

For compliance officers, that means you can define guardrails such as:

• •only approved systems may be queried
• •certain decisions require human approval
• •every tool call must be logged with timestamp and case ID
• •sensitive fields must be masked before being shown to the model

A basic control table might look like this:

Related Concepts

• •

  Tool use / function calling
  How agents invoke APIs, databases, or services during execution.

• •

  Retrieval-Augmented Generation (RAG)
  A pattern where the model retrieves documents before answering; often used alongside ReAct.

• •

  Human-in-the-loop workflows
  Review checkpoints where people approve or override model outputs.

• •

  Agent guardrails
  Rules that limit what an agent can see, do, or decide.

• •

  Audit logging for AI systems
  Capturing prompts, tool calls, outputs, and approvals for oversight and investigations.

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.