ReAct Agent: Guide to understand its functionalities and create it from scratch

As we move further and further into the AI era, we are encountering a time when AI agents are beginning to mirror this fundamental aspect of human behavior. By incorporating tool use and complex reasoning, these agents are pushing the boundaries of what machines can achieve, potentially revolutionizing the way AI interacts with its surroundings.

In this article, we will explore in detail what ReAct Agents are, their characteristics, expectations, and how they can improve your business.

AI agents are designed to perceive the environment, reason about it, and execute actions to achieve specific goals. They work by decomposing complex objectives into manageable subtasks, selecting appropriate tools (search engines, databases, code execution environments, etc.) for each subtask, and executing these tools iteratively while analyzing the resulting observations.

The agent adapts its strategy based on intermediate results, refines inputs to optimize tool usage, and maintains a historical context to avoid repeating ineffective approaches.

Unlike traditional AI systems that separate decision making from execution, ReAct agents follow a continuous cycle of reasoning and action. Their operation is based on the following phases:

1. Input: The agent receives a natural language description of the task, which is entered into the main LLM.
2. Reasoning: The LLM breaks the task into smaller steps, analyzes the situation, considers the available information, and plans the actions needed to complete it.
3. Action: Based on the reasoning, the LLM decides which tool to use and executes actions to gather information or interact with the external environment.
4. Observation: The agent observes the results of the actions and updates its knowledge accordingly. Furthermore, it uses this new information to refine its reasoning in the next iteration.
5. Response: generates a final response based on the reasoning and information gathered.

The ReACT process is iterative. The agent continually alternates between reasoning and action, refining its plan as it gathers more information from external environments. Based on the new information, it may decide to adjust its strategy or explore different avenues to achieve the desired outcome.

And, by interacting with the external world, it can continually update its knowledge base, improving its reasoning and decision-making in subsequent iterations.

ReAct is a powerful framework for creating AI agents that integrates reasoning and decision-making with task execution. By leveraging large language models (LLMs), ReAct agents can dynamically analyze problems, choose the right tools, and work iteratively to find solutions.

It is inspired by the way we humans can intuitively use natural language in the step-by-step planning and execution of complex tasks. Instead of implementing rule-based or predefined workflows, these agents look to the reasoning capabilities of their LLM to dynamically adjust their approach based on new information or the results of previous steps.

This approach enables AI agents to combine thought processes and actions, potentially leading to more efficient and adaptive AI systems.

• Seamless reasoning and action: ReAct agents use LLM as centralized components that reason about the environment and determine appropriate actions simultaneously. This unification enables the agent to seamlessly process observations, generate plans, and execute actions, eliminating the need for separate, manually designed modules. As a result, it can adapt more smoothly to complex and dynamic environments.
• Dynamic use of tools: These agents can incorporate a variety of external tools and APIs, selecting and using them according to the current context and objectives. The LLM facilitates tool selection by analyzing the user’s task and previous observations to determine the most appropriate resources. This integration allows the agent to expand its capabilities on the fly.
• Iterative Problem Solving: This framework allows agents to tackle complex tasks through an iterative cycle of thought, action, and observation. This cycle allows the agent to evaluate the results of its actions, refine its strategies according to their effectiveness, and plan subsequent steps accordingly. LLM uses current and historical observations to inform decision-making, and the incorporation of a memory component further enhances the agent’s adaptability and learning.

Therefore, ReAct agents overcome the limitations of traditional architectures, especially in scenarios that require flexible reasoning and adaptive behavior. The fusion of reasoning and action within an LLM-centric framework enables more sophisticated and contextualized problem-solving capabilities.

The introduction of the ReAct framework has been an important step in the advancement of LLM-based agency workflows. From integrating LLMs into real-time external information using RAG to contributing to later advances that led to modern reasoning models, ReAct has helped drive the use of LLMs for tasks far beyond text generation.

Its advantages are numerous, the most important of which are:

• Versatility: These agents can be configured to work with a wide variety of external tools and APIs.
• Adaptability: This versatility, coupled with the dynamic and situational nature of how they determine the right tool or API to call, allows ReAct agents to use their reasoning to adapt to new challenges. Furthermore, by operating within an extended context window or with external memory, they can learn from past mistakes and successes, making them flexible and resilient.
• Explainability: the verbalized reasoning process of a ReAct agent is easy to follow, which facilitates debugging and helps make it relatively easy to build and optimize.
• Accuracy: chain-of-thought (CoP) reasoning alone offers numerous advantages for LLMs, but also an increased risk of hallucinations. Combining CoP in ReAct with an external connection to information sources significantly reduces hallucinations, which increases the accuracy and reliability of these agents.
• Transparency and trust: they are designed with transparency as a priority, allowing users to observe their reasoning processes and actions, which builds trust and facilitates understanding of the agents.

Both ReAct and function call agents are powerful frameworks that extend the capabilities of LLMs, allowing them to interact with the real world. However, they differ in their specific focus and strengths.

Below, you can see a comparative analysis:

Comparison table by LeewayHertz

ReAct agents, thanks to their ability to combine reasoning and action, offer a wide range of applications, and some of the most common use cases include:

• Customer service: ReACT agents can process complex customer queries, access relevant information, and provide accurate and helpful responses. In addition, by combining reasoning with actions such as checking order status or initiating refunds, these agents can accelerate customer service problem resolution.
• Information retrieval: Agents can tackle difficult multi-hop questions by breaking them down into smaller steps, searching for relevant information, and synthesizing answers. In addition, they can generate concise and informative summaries.
• Personal assistants can help users manage their schedules, set reminders, and plan events. In fact, by analyzing user preferences and available options, these agents can offer personalized recommendations for products, services, or activities.
• Education: They can adapt to individual learning styles and provide personalized explanations and exercises. They can also provide constructive feedback and evaluate performance.
• Financial analysis: They can process financial data, identify trends, and generate information.
• Medical diagnosis: By combining medical knowledge with patient data, they can assist in diagnosis and treatment planning.
• Creative writing: They can generate creative text formats such as poems, scripts, codes, musical pieces, emails, letters, and so on…

All in all, there are endless possibilities with these types of agents, and Plain Concepts can help. We help you design your strategy, protect your environment, choose the best solutions, close technology and data gaps, and establish rigorous oversight to achieve responsible AI. You can achieve rapid productivity gains and build the foundations for new business models based on hyper-personalization or continuous access to relevant data and information.

We have a team of experts who have been successfully applying this technology in numerous projects, ensuring the security of customers. We have been bringing AI to our clients for more than 10 years, and now we propose a Framework for the adoption of generative AI:

• Unlock the potential of end-to-end generative AI.
• Accelerate your AI journey with our experts.
• Understand how your data should be structured and governed.
• Explore generative AI use cases that fit your goals.
• Create a tailored plan with realistic timelines and estimates.
• Build the patterns, processes, and teams you need.
• Deploy AI solutions to support your digital transformation.

Don’t wait any longer and turn AI agents into your best ally!