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

ReAct is an AI agent pattern that combines Reasoning and Acting in a loop: the model thinks about the task, takes an action with a tool, then uses the result to decide the next step. In practice, ReAct lets an agent solve multi-step problems by alternating between internal reasoning and external actions like searching, querying a policy system, or calling an underwriting API.

How It Works

Think of ReAct like a claims manager handling a complex case.

They do not stare at one document and guess. They review the claim, check the policy system, call for missing evidence, inspect the medical report, then decide what to do next. Each step changes what they know.

That is ReAct:

• •Reason: The agent decides what it needs next.
• •Act: It uses a tool to get that information or perform an action.
• •Observe: It reads the result.
• •Repeat: It updates its plan and continues until it can answer or complete the task.

For insurance teams, this matters because most useful workflows are not one-shot prompts. They require multiple system calls:

• •policy administration system
• •claims platform
• •CRM
• •document store
• •fraud rules engine
• •pricing or underwriting services

A plain LLM can draft a response. A ReAct agent can actually work through the problem.

A simple analogy: imagine a CTO asking an engineer to troubleshoot an outage.

A bad engineer guesses from memory. A good engineer checks logs, inspects metrics, tests hypotheses, and only then fixes the issue. ReAct gives the model that same operational discipline.

What it looks like in practice

A ReAct loop usually follows this pattern:

1. •User asks a question or gives a task.
2. •The model reasons about what information is missing.
3. •The model selects a tool.
4. •The tool returns data.
5. •The model updates its reasoning.
6. •The loop continues until completion.

Example tool calls might include:

• •get_policy_details(policy_id)
• •search_claim_documents(claim_id)
• •check_fraud_score(claim_id)
• •estimate_loss_reserve(claim_id)

The important part is not the prompt wording. It is that the agent can interleave thought and action instead of producing a single static answer.

Why It Matters

CTOs in insurance should care because ReAct maps well to real operational workflows.

• •

  Better handling of incomplete data

  • •Insurance decisions often depend on missing context.
  • •ReAct lets an agent fetch what it needs instead of hallucinating from partial input.
• •

  Works with enterprise systems

  • •Most value sits behind APIs and internal tools.
  • •ReAct is designed for tool use, which makes it practical for claims, underwriting, servicing, and broker support.
• •

  More auditable than free-form chat

  • •You can log each reasoning step and each action taken.
  • •That gives you a clearer trail for QA, compliance review, and post-incident analysis.
• •

  Supports complex workflows

  • •Straight Q&A is easy.
  • •Multi-step tasks like “review this claim against policy terms and flag exceptions” are where ReAct earns its keep.

For engineering leaders, this also means cleaner control boundaries:

Real Example

Let’s say a property insurer wants an agent to assist with first notice of loss triage for water damage claims.

The user asks:

“Can we approve temporary accommodation for claim CLM-48291?”

A ReAct-based agent would not answer immediately. It would work through the case.

Step-by-step flow

1. •

   Reason

   • •The agent identifies it needs policy coverage details, claim status, and any exclusion flags.
2. •

   Act

   • •Calls get_claim(CL M-48291)
   • •Calls get_policy(policy_id)
   • •Calls search_documents(claim_id)
3. •

   Observe

   • •Finds:
     • •active homeowner’s policy
     • •accidental escape of water covered
     • •temporary accommodation limit available
     • •no exclusion currently flagged
     • •adjuster notes indicate uninhabitable kitchen area
4. •

   Reason

   • •The agent sees there is enough evidence to recommend approval within limits.
   • •It checks whether pre-authorisation threshold requires human review.
5. •

   Act

   • •Calls check_authorisation_limit(claim_id)
   • •If below threshold: drafts approval note
   • •If above threshold: routes to supervisor queue
6. •

   Final response

   • •“Temporary accommodation appears covered under section X of the policy. Estimated cost is within authorisation limit; recommend approval pending standard validation.”

This is useful because the model did not invent coverage terms or guess based on language alone. It used actual systems to gather evidence before making a recommendation.

For insurance operations, that difference matters.

A chatbot answers questions. A ReAct agent executes controlled work inside your business processes.

Related Concepts

• •

  Tool calling / function calling

  • •The mechanism that lets an LLM invoke external systems like APIs or databases.
• •

  Agent orchestration

  • •The higher-level control layer that manages loops, retries, guardrails, and stopping conditions.
• •

  Chain-of-thought prompting

  • •A reasoning style related to ReAct, but without necessarily taking actions in between steps.
• •

  RAG (Retrieval-Augmented Generation)

  • •Useful when the main need is fetching documents or knowledge before answering.
  • •ReAct goes further by letting the model act on systems as well as retrieve text.
• •

  Workflow automation

  • •Traditional deterministic automation still matters.
  • •ReAct sits above it when decisions need context-sensitive branching and human-in-the-loop escalation.

If you are evaluating AI agents for insurance, start here: ask whether your target workflow needs only retrieval, or whether it needs reasoning plus controlled system actions. If it needs both, you are in ReAct territory.

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.