A ReACT agent LLM is an AI model combining reasoning and actions to enable dynamic problem-solving, by thinking step-by-step and working with other tools.
ReACT stands for Reasoning and Acting. It’s a key component of generative AI (GenAI) frameworks, like Retrieval-Augmented Generation (RAG), designed to make your enterprise LLM (Large Language Model) more accurate and reliable. ReACT works by alternating between thinking (via chain-of-thought reasoning) and acting (executing tasks). It doesn’t just generate answers. It also uses various tools to interact with external sources (search engines, databases, knowledge bases, etc.) to gather new information and refine its responses.
What makes a ReACT agent LLM so special is its dynamic, step-by-step approach. The model starts with a thought – for example, a complex question or task. It then takes an action, like performing a search, and uses the result (or observation) to break down the question, adjust its reasoning, and decide on its next action. This back-and-forth process mirrors how humans tackle problems. It uses reasoning to guide actions, and then uses actions to provide insights for the next step in the reasoning-action-observation cycle.
A ReACT agent, one of many different LLM agents, solves problems by combining reasoning with action. Unlike a traditional LLM, that relies solely on the publicly available data it was trained on, a ReACT agent LLM uses a RAG architecture to gather trusted company information (structured data and unstructured docs, for example) to refine its understanding.
For example, if asked a complex question, a ReACT agent LLM might first think about how to break the task down into smaller parts. It might then search for relevant information online, or in your enterprise systems and knowledge bases. It would use the findings of this search to guide its next steps – alternating between thought and action – until it arrives at a well-supported answer.
ReACT agents allow LLMs to dynamically address complicated tasks – while reducing errors and delivering more reliable, and easier-to-understand responses. ReACT agent LLMs are especially valuable for queries that require both critical thinking and up-to-date information.
ReACT augments chain-of-thought prompting by enabling the incorporation of real-time information as part of the iterative reasoning-action loop. Although CoT prompting enables large language models to break down tasks into step-by-step sub-tasks, it has one key limitation: It’s knowledge is limited to the LLM’s training data. This dependence on static data can lead to AI hallucinations when fresh, trusted information is required.
A ReACT agent LLM addresses this issue by being able to take action. After reasoning out a task, the ReACT agent LLM then performs an action – like searching for particular information or interacting with a relevant database. It uses the results of this action to refine its reasoning or determine what to do next. Essentially, a ReAct agent LLM goes beyond chain-of-thought reasoning, by accessing external data when needed, reducing errors, and improving reliability. So, using ReACT agents for tasks requiring logical reasoning and real-world interaction is highly effective.
A ReACT agents LLM takes a systematic approach to handling complex tasks that may include interaction with external tools or information sources. Following a step-by-step process, it:
Receives a prompt
The process starts with a prompt or question from the user. This prompt outlines the task the ReACT agent needs to solve. The prompt can include examples of how reasoning and actions should be alternated, helping guide the ReACT agent's response.
Thinks through the task
The ReACT agent begins by reasoning out the task. It generates an internal thought, which helps it break the problem into smaller, manageable steps. This initial reasoning stage can involve identifying missing information, planning actions, or just outlining the logical steps required to solve the task.
Takes action
Based on its thought process, the ReACT agent then takes action, such as searching online or in your enterprise systems or knowledge bases. For example, if the task involves historical data, the agent could initiate a search in the most trusted and relevant sources.
Observes the result
Once the action is completed, the ReACT agent receives an observation, or the outcome of the action. This outcome could include search results, data retrieved from an API, or any other relevant feedback. This observation then serves as new information for the agent to incorporate into its reasoning-action-observation loop.
Refines its reasoning
The agent then evaluates the observation to decide on next step. It can choose to update its reasoning, revise its plan, or determine that additional actions are needed. This process allows the agent to dynamically adjust its approach as new information comes to light.
Repeats the cycle
The agent continues alternating between thoughts, actions, and observations as needed. If the task requires multiple pieces of information, the ReACT agent will go through several reasoning-action-observation rounds until it gathers all the necessary data or resolves the issue.
Synthesizes the answer
Once the agent has enough information and has completed its reasoning, it synthesizes a response based on its own reasoning and on the data it gathered during its actions. Ideally, it creates a well-supported, accurate answer that reflects both logical thinking and up-to-date information.
Delivers the response
Lastly, the ReACT agent provides the answer to the user in a clear and concise format. Thanks to the thought-action-observation cycle, a ReACT agent LLM provides adaptable and reliable responses that are well-suited to real-world applications, where accuracy and up-to-date information are essential.
The K2view RAG tool, GenAI Data Fusion, has ReACT agent LLM capabilities built in. It enhances the accuracy and security of your GenAI applications by:
Integrating real-time data about specific customers or business entities into prompts.
Masking sensitive or PII (Personally Identifiable Information) dynamically.
Processing data service access requests and suggesting cross-/up-sell recommendations.
Collecting data from multiple source systems via API, CDC, messaging, or streaming.
Discover GenAI Data Fusion, the RAG tool
with ReACT agent LLM functionality built in.