Hi! Been introduced to AI agents already? Amazing! If not, no stress—start your journey here 👉 What Are AI Agents?

We discussed how AI agents aren’t just passive tools; they think, decide, and act to accomplish goals with minimal human input. They’re changing the way we work, learn, and even rest. But behind this breakthrough in autonomy lies a more technical foundation—one that’s easy to overlook but worth understanding.

Before AI agents could reason on their own or act on our behalf, they had to be taught how to think and do and, more importantly, combine both in a meaningful, repeatable way.

The Missing Link in AI Reasoning and Action

Large Language Models (LLMs) have reshaped the landscape of artificial intelligence. Whether powering chatbots, generating creative content, translating documents, or assisting developers with code, LLMs have redefined what’s possible in tech. Their rapid development proves we’re only scratching the surface of what this technology can achieve.

However, despite all that promise, traditional LLMs have a significant limitation: they treat reasoning and action as 2 (two) separate processes. This separation works well for straightforward tasks, but the cracks begin to show when handling real-world problems that require ongoing decisions, learning, and adapting.

Let’s say you’re chatting with a customer service bot. You ask about a return policy. The bot pulls from its internal knowledge base (reasoning) and replies with a prewritten policy. Then, you, as the customer, request to initiate the return. Instead of smoothly continuing the conversation and taking action, it redirects you to another form or service (action). The reasoning and action happen in two separate steps—and that disconnect limits the experience.

Where ReAct Fits in the AI Ecosystem

Before diving deeper, let’s define where everything stands.

Think of AI agents as the broad umbrella — autonomous systems designed to interpret goals, make decisions, and carry out tasks with minimal human input. They can take real-world actions based on high-level instructions without being guided.

Within that category, we have LLM agents — powered by large language models like GPT — that use natural language to understand, plan, and respond. These agents bring reasoning and communication into the equation.

Then, we have ReAct agents, a subset of LLM agents with a twist: built using the ReAct framework. That means they don’t just reason or act — they do both in a continuous feedback loop. This hybrid process allows them to reflect, learn from past steps, and adjust their actions more fluidly.

In short:

• AI Agents – a broad category of autonomous systems
• LLM Agents – agents that think and communicate using language models
• ReAct Agents – LLM agents that combine reasoning + action for smarter decisions

That’s where ReAct fits in: not just another agent type but a more innovative, integrated approach — bridging the gap between thought and action in the LLM space.

What is the ReAct Framework?

We’ve touched on ReAct briefly in a previous article — but now, it’s time to dig deeper. 

ReAct is not that “React” that you’re thinking… 🤓

ReAct stands for Reasoning + Acting. It’s a robust framework designed to help language agents solve complex tasks not just by thinking or doing — but by doing both in a seamless, ongoing loop.

So, what does that mean?

In simple terms, ReAct enables large language models to generate both reasoning traces (like internal thoughts) and action steps (like using tools or querying data) within the same sequence. This framework allows agents to reason through problems while actively engaging with their environment — helping them solve tasks more effectively, adapt in real-time, and revise their responses along the way.

Traditionally, agents either reason internally (Chain-of-Thought processing) or take external actions (like calling a tool). However, with ReAct, the agent switches between thinking and acting — like how humans naturally solve problems.

We act, see what happens, and then adjust.

How the ReAct loop works:

• 🧠 Thought – The agent reasons about the problem, like brainstorming or planning internally.
• ⚙️ Action – Based on that reasoning, it performs a step — maybe calling a tool, running code, or searching for data.
• 👀 Observation – It checks the result of that action. Was it helpful? Did it change anything?

This cycle then repeats — Thought → Action → Observation — forming a loop where the agent learns from each step and improves its next move. This makes ReAct agents far more adaptable, accurate, and human-like in interacting with the world.

React in Action

Remember the earlier customer service chatbot example? You ask about the return policy, and the bot quickly gives you a prewritten answer. But the moment you try to start a return, it redirects you to a third-party page without helping further. Reasoning and action happen in separate processes, making the whole experience feel clunky and disconnected.

Now, let’s picture it the ReAct way:

You ask about the return policy.

All of that — without breaking the conversation.

The experience feels smooth, responsive, and much more like talking to a helpful human agent who’s thinking things through and taking real action at every step.

How ReAct Works in Practice

4 (four) main styles used in prompting agents today:

💻 Standard Prompting

To begin with, this is the most basic style of prompting. In this approach, the model tries to answer the question immediately, without any internal reasoning or external action.

• How it works: The agent receives a prompt and generates an answer immediately.
• Limitations: No thinking, no exploring. The agent might jump to the wrong conclusions or overlook important details if the question is complex.

💻 Chain-of-Thought (CoT) 

Chain-of-Thought prompting is a technique that encourages the model to generate a detailed, step-by-step reasoning process, explicitly laying out every part of its thought path before producing a final answer.

• How it works: The agent reasons step-by-step internally but still doesn’t interact with external tools or environments.
• Limitations: Useful for better thinking, but stuck if outside information is needed.

💻 Acting-Only

Acting-only models interact with tools or their environments but without reasoning. This is similar to how WebGPT (OpenAI’s earlier web-scraping agent) operated.

• How it works: The agent takes actions (like searching online) but doesn’t plan or explain its moves.
• Limitations: Can perform tasks but easily gets stuck or confused without thoughtful reasoning.

💻 ReAct – Reasoning + Acti0n

ReAct combines the strengths of both Chain-of-Thought and Acting.

• How it works: The agent thinks through the problem and simultaneously interacts with the environment or tools.
• Benefits: Far better at adapting, correcting mistakes, and solving complex tasks smoothly.

Figure 1. Comparison of 4 prompting methods used in agents by solving a HotpotQA question

*HotpotQA is a question-answering dataset that includes multi-hop questions and provides supervised supporting facts to enhance the understandability of answer systems (Yang et al., 2018).

In summary:

• Standard Prompting: Direct answer based on internal knowledge.

• Chain-of-Thought (CoT): Step-by-step reasoning through facts to reach an answer.

• Acting Only: Searching or verifying facts to gather the information needed for an answer.

• ReAct (Reason + Act): Iterating between reasoning and acting to refine the answer with external data.

Planning Approaches in ReAct Agents

What makes ReAct agents even more powerful is how they plan their actions. Instead of reacting mindlessly, agents use several intelligent planning techniques:

🧩 Task Decomposition

• Rather than tackling everything at once, ReAct agents break complex problems into smaller, manageable steps — making them easier to solve, one piece at a time.

🧠 Multi-Plan Selection

• They look back on previous thoughts, actions, and outcomes to learn from them — adjusting their next steps based on what worked (or didn’t).

• “Last time, I searched incorrectly; this time, I’ll refine my query.”

🔄 Reflection

• They review their past thoughts, actions, and observations to learn what’s working — and what isn’t.

• “Last time I searched incorrectly; this time I’ll refine my query.”

🛠️ External Modules

• Instead of relying solely on internal knowledge, ReAct agents can reach out — they will use tools like search engines, databases, or APIs to retrieve fresh, relevant information when needed.

🧾 Memory-Based Strategies

• With access to past interactions and known facts, agents can stay consistent, track context over time, and avoid repeating mistakes — a crucial part of long-term reasoning.

These planning strategies make ReAct agents far more goal-aware, adaptive, and resilient than traditional agents.

How ReAct Is Shaping the Future of AI Systems

ReAct in Agentic Workflows

This framework is a key enabler of agentic workflows, where language models evolve from passive responders into proactive problem-solvers. These agents don’t simply follow instructions; they reason, make decisions, reflect on outcomes, and adapt their next move based on context.

Grounded AI Through RAG

It plays a crucial role in grounding AI systems. When paired with retrieval-augmented generation (RAG), agents don’t just depend on static training data — they can pull live information, cross-check outputs, and operate with up-to-date facts. Practices like this significantly reduce hallucinations and improve factual accuracy.

Inspiring More Adaptive Frameworks

The ReAct model has sparked the development of other frameworks like Reflexion, which builds on ReAct’s reasoning-action loop by adding self-evaluation and iterative learning. These frameworks are helping agents not just act — but learn from every action they take.

“ReAct is a conceptual framework for building AI agents that can interact with their environment in a structured but adaptable way.”  — IBM.

How we design language-based AI is evolving, and ReAct is at the forefront of that shift. By combining reasoning and action in a single, ongoing loop, ReAct agents learn and adapt in real-time. This approach improves accuracy, reduces hallucinations/errors in LLMs, and helps agents make context-aware, grounded decisions. Unlike traditional models that generate text, ReAct agents bring structured decision-making into tools, APIs, and workflows. They can handle much more complex tasks — from planning and searching to scheduling and solving multi-step problems. More than just a framework, ReAct represents a foundation for building more capable, reliable, and thoughtful AI systems.

References

• Part 1: ReACT AI Agents: A Guide to Smarter AI Through Reasoning and Action
• What is a ReAct agent?
• REACT: SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS
• HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering