What is ReAct in AI Agents? A Guide for developers in wealth management

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, observe the result, and then decide what to do next. In practice, ReAct lets an agent use tools like search, databases, calculators, or internal APIs instead of trying to answer everything from memory.

How It Works

ReAct stands for Reason + Act.

The basic loop looks like this:

• •The agent receives a user request
• •It reasons about what information it needs
• •It takes an action using a tool
• •It observes the result
• •It reasons again with the new context
• •It repeats until it can produce a final answer

A simple way to think about it is like a wealth advisor preparing for a client meeting.

They do not walk into the room and guess the portfolio status from memory. They:

• •check holdings
• •review recent transactions
• •look at market movements
• •compare against risk constraints
• •then make a recommendation

That is ReAct. The “reasoning” is deciding what to check next. The “acting” is actually checking it.

For developers in wealth management, this matters because most useful agent workflows are not single-shot prompts. They are multi-step workflows with dependencies:

• •“Find the client’s current allocation”
• •“Check whether the position breaches policy”
• •“Pull market data for the affected assets”
• •“Draft a response for the advisor”

ReAct gives you a clean pattern for that kind of work.

A typical implementation uses structured tool calls rather than free-form text. The model might output something like:

{
  "thought": "I need the client's current equity exposure before I can assess suitability.",
  "action": "get_portfolio_exposure",
  "input": {
    "client_id": "C12345"
  }
}

Then your system returns the observation:

{
  "equity_exposure": 0.74,
  "risk_profile": "moderate",
  "policy_limit": 0.70
}

The agent continues from there until it has enough evidence to answer.

Why It Matters

Developers in wealth management should care because ReAct fits real production constraints better than plain chat responses.

• •

  It reduces hallucination risk

  The agent does not need to invent facts when it can query systems of record. That matters when answering questions about balances, allocations, fees, or suitability rules.

• •

  It supports auditable workflows

  Each step can be logged: what the agent thought, which tool it used, and what data came back. That gives compliance and operations teams something they can review.

• •

  It handles multi-step business logic

  Wealth workflows often require branching logic across CRM, portfolio systems, pricing engines, and policy rules. ReAct gives you a way to orchestrate those steps without hardcoding every path.

• •

  It improves user experience

  Instead of returning a vague answer like “I’m checking,” the agent can actively gather context and produce a useful result in one interaction.

Real Example

Let’s say an advisor asks:

“Can we recommend rebalancing for client A without violating their risk mandate?”

A ReAct agent could handle this as follows:

1. •Reason: It needs current holdings and target allocation.
2. •Act: Call get_client_portfolio(client_id).
3. •Observe: Receive current asset weights.
4. •Reason: It needs the client’s risk mandate and concentration limits.
5. •Act: Call get_investment_policy(client_id).
6. •Observe: Receive constraints such as max equity exposure and single-name limits.
7. •Reason: It should compare current weights to target weights and policy rules.
8. •Act: Call calculate_rebalance_trade_list(portfolio, target_model).
9. •Observe: Receive proposed trades.
10. •Reason: Check whether any trade violates policy.
11. •Act: Call validate_trades_against_policy(trades, policy).
12. •Observe: Validation result.
13. •Final response: Summarize whether rebalancing is allowed and list the trades.

In practice, this could look like:

{
  "request": "Recommend rebalancing for client A without violating risk mandate",
  "steps": [
    {"tool": "get_client_portfolio", "result": "..."},
    {"tool": "get_investment_policy", "result": "..."},
    {"tool": "calculate_rebalance_trade_list", "result": "..."},
    {"tool": "validate_trades_against_policy", "result": {"status": "pass"}}
  ],
  "answer": {
    "status": "approved",
    "summary": "Proposed rebalance stays within equity exposure limits and respects concentration rules.",
    "trades": [
      {"symbol": "ETF1", "action": "buy", "quantity": 120},
      {"symbol": "ETF2", "action": "sell", "quantity": 80}
    ]
  }
}

The key point: ReAct does not replace your portfolio logic or policy engine. It coordinates them.

That makes it useful in banking and insurance too:

• •claims triage agents that query policy coverage before responding
• •KYC assistants that pull identity records before asking follow-up questions
• •advisor copilots that fetch performance data before drafting commentary

Related Concepts

• •

  Tool calling / function calling

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

• •

  Chain-of-thought reasoning

  The reasoning side of ReAct, though in production you usually keep this structured and controlled rather than exposing raw internal thoughts.

• •

  Agent orchestration

  Managing multiple steps, tools, retries, timeouts, and state across an entire workflow.

• •

  RAG (Retrieval-Augmented Generation)

  Useful when the agent needs documents or policies from a knowledge base rather than live system actions.

• •

  Workflow engines

  BPMN engines or state machines often sit underneath ReAct-style agents when you need strict control over branching and approvals.

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.