What is ReAct Pattern?
The ReAct Pattern is an AI reasoning framework that combines Reasoning and Acting in an interleaved loop, where the AI model thinks about what to do, takes an action, observes the result, and then reasons again about the next step, enabling more reliable and transparent problem-solving.
What Is the ReAct Pattern?
The ReAct Pattern — short for Reasoning + Acting — is an AI framework where a model alternates between thinking about a problem and taking actions to solve it. Instead of either reasoning in isolation or acting without thought, ReAct interleaves these two processes: the AI thinks, then acts, then observes the result, then thinks again about what to do next.
This mirrors how effective human problem-solvers work. A detective does not solve a case by thinking alone in a room. They form a hypothesis, investigate, review the evidence, revise their hypothesis, and investigate further. ReAct gives AI agents this same iterative, grounded approach to problem-solving.
How the ReAct Pattern Works
A ReAct loop follows a consistent cycle:
Step 1: Thought
The agent reasons about the current situation, the goal, and what information or action is needed next. This thought is explicitly articulated: "I need to find the customer's order history to understand the refund request. I should query the order management system."
Step 2: Action
Based on its reasoning, the agent takes a specific action — querying a database, calling an API, searching the web, or performing a calculation.
Step 3: Observation
The agent receives and processes the result of its action. It examines the data returned, the status of the operation, or any errors encountered.
Step 4: Repeat
The agent uses the observation to inform its next thought. It may have enough information to proceed to the next sub-task, or it may need to take additional actions. This loop continues until the overall task is complete.
A Practical Example
Imagine an AI agent tasked with answering: "What was our best-selling product in Thailand last quarter, and how does it compare to the same period last year?"
Thought 1: I need to find sales data for Thailand for the last quarter. I should query the sales database.
Action 1: Query sales database for Thailand Q4 2025 data, sorted by revenue.
Observation 1: The top product was "Smart Home Hub Pro" with THB 45 million in revenue from 12,000 units.
Thought 2: Now I need the same data for Q4 2024 to make the comparison.
Action 2: Query sales database for Thailand Q4 2024, filtering for "Smart Home Hub Pro."
Observation 2: In Q4 2024, Smart Home Hub Pro generated THB 28 million from 7,500 units.
Thought 3: I now have both data points. I should calculate the growth rates and prepare a clear comparison.
Action 3: Calculate year-over-year growth: revenue up 60.7%, units up 60%.
Observation 3: Calculations complete.
Final Answer: The best-selling product in Thailand last quarter was the Smart Home Hub Pro, generating THB 45 million in revenue from 12,000 units. This represents a 60.7% increase in revenue and 60% increase in unit sales compared to Q4 2024.
Why ReAct Is Important
The ReAct pattern addresses several fundamental challenges in AI systems:
Grounded Reasoning
Pure reasoning without access to external information can lead to hallucination — the AI generates plausible-sounding but incorrect answers. By requiring the agent to act and observe before continuing its reasoning, ReAct keeps the reasoning grounded in actual data and real-world results.
Transparent Decision-Making
Because each thought and action is explicitly recorded, the entire problem-solving process is visible and auditable. Stakeholders can see exactly why the agent made each decision and verify the information it used.
Adaptive Problem-Solving
Unlike rigid workflows, the ReAct pattern allows the agent to adapt its approach based on what it discovers. If an initial action produces unexpected results, the agent can reason about why and adjust its strategy accordingly.
Error Recovery
When an action fails, the agent can reason about the failure and try an alternative approach. This self-correcting behavior makes ReAct-based agents significantly more robust than agents that follow fixed action sequences.
ReAct in the Southeast Asian Business Context
The ReAct pattern is well-suited for ASEAN business environments because:
- Navigating diverse data sources — When researching market opportunities across ASEAN, an agent may need to query different data sources for each country. ReAct allows it to adapt its search strategy based on what information is available in each market.
- Handling uncertainty — Business data in emerging markets is often incomplete or inconsistent. ReAct enables agents to recognize gaps, seek alternative sources, and adjust their conclusions based on available evidence.
- Multi-market analysis — Comparing business performance across countries like Singapore, Indonesia, and Vietnam requires iterative data gathering and cross-referencing. The ReAct loop naturally supports this kind of multi-step, multi-source analysis.
- Regulatory research — Understanding compliance requirements across different ASEAN jurisdictions often requires checking multiple regulatory databases, verifying currency of information, and cross-referencing rules. ReAct enables systematic, thorough research.
ReAct vs. Other AI Patterns
Understanding how ReAct compares to alternative approaches:
ReAct vs. Pure Reasoning (Chain of Thought)
Chain of thought reasoning is powerful but operates purely on the model's internal knowledge. ReAct extends this by adding external actions, so the reasoning is grounded in real data rather than just training data.
ReAct vs. Pure Acting
Some agent architectures skip explicit reasoning and simply take actions based on the input. This can be faster but is less transparent and more prone to errors because the agent does not pause to plan or evaluate.
ReAct vs. Plan-then-Execute
In a plan-then-execute approach, the agent creates a complete plan upfront and then follows it. ReAct is more flexible because the agent adjusts its plan based on the results of each action, making it better suited for tasks where conditions change or information is initially incomplete.
Implementing ReAct
To implement the ReAct pattern in your AI applications:
- Enable tool access — ReAct requires the agent to take actions, so it needs access to relevant tools, APIs, and data sources
- Configure thought visibility — Set up your system to capture and display the agent's reasoning steps for transparency
- Set iteration limits — Define a maximum number of thought-action cycles to prevent infinite loops
- Implement guardrails — Define which actions require human approval before execution
- Monitor performance — Track the number of cycles needed to complete tasks and the accuracy of final results
Key Takeaways for Decision-Makers
- ReAct is the standard pattern for building reliable, transparent AI agents that solve complex problems
- It combines the best of reasoning and action, producing more accurate results than either approach alone
- The transparency of the ReAct loop makes it suitable for business applications where auditability matters
- Most major AI agent frameworks implement ReAct or similar patterns by default
The ReAct pattern is the foundational architecture behind most modern AI agents, making it important for business leaders to understand even if they never implement it directly. For CEOs, the key insight is that ReAct-based agents are fundamentally more trustworthy than AI systems that produce answers without showing their work. When an agent explicitly reasons through a problem, gathers evidence, and builds its answer step by step, you can evaluate the quality of its work — just as you would evaluate a human analyst's report.
For CTOs, the ReAct pattern provides the architectural blueprint for building enterprise-grade AI agents. It solves the critical problem of AI hallucination by grounding reasoning in real data, and it provides natural audit trails that satisfy compliance and governance requirements. When evaluating AI agent platforms and frameworks, understanding ReAct helps you assess whether a platform's approach to agent reasoning is sound and suitable for business-critical applications.
In Southeast Asia, where businesses frequently need to gather and analyze information across diverse, fragmented data sources, the ReAct pattern is especially well-suited. Its ability to adapt the research strategy based on what information is actually available — rather than assuming a fixed set of data sources — makes it robust for the realities of operating across the ASEAN region. Leaders who understand this pattern will be better equipped to evaluate AI solutions, set appropriate expectations, and design effective AI-powered workflows.
- Understand that most AI agent platforms use ReAct or similar patterns under the hood — this knowledge helps you evaluate platforms more effectively
- Require transparency in your AI agent deployments — ensure the reasoning and action steps are logged and reviewable
- Set appropriate iteration limits to control costs and prevent agents from getting stuck in unproductive loops
- Design your data sources and APIs to return clear, informative responses that agents can reason about effectively
- Monitor the average number of reasoning-action cycles for different task types to optimize performance and cost
- Use ReAct transparency for quality assurance — review agent reasoning chains regularly to identify patterns of errors or inefficiencies
- Balance agent autonomy with human oversight by defining which actions can be taken automatically and which need approval
Frequently Asked Questions
Is ReAct the same as the React JavaScript framework?
No, these are completely different concepts that share a name. React (or React.js) is a JavaScript library for building user interfaces, created by Facebook. The ReAct Pattern in AI stands for Reasoning + Acting and is a framework for how AI agents solve problems by alternating between thinking and taking actions. The naming is coincidental and can cause confusion, but they operate in entirely different domains.
Do all AI agents use the ReAct pattern?
Not all AI agents use ReAct explicitly, but most modern agent architectures incorporate similar principles of interleaving reasoning with action. Some agents use simpler patterns for straightforward tasks, and some use more complex multi-agent architectures for sophisticated problems. However, the core idea of think-act-observe has become the standard approach in agentic AI because it consistently produces more reliable results than alternatives.
More Questions
ReAct reduces hallucination by grounding the AI's reasoning in real data obtained through actions. Instead of generating an answer purely from its training data, which may be outdated or incomplete, the agent queries actual databases, searches current sources, and verifies information before incorporating it into its reasoning. Each observation provides a reality check that corrects or confirms the agent's reasoning, significantly reducing the likelihood of fabricated or inaccurate information in the final output.
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