How ReAct agents can transform the enterprise

How a ReAct agent works

ReAct agents use an LLM's reasoning capabilities to adapt their planning or decisions based on changing information or the results of previous actions. ReAct agents are a central part of agentic AI systems, and operate using an iterative loop involving the following three principal phases:

1. Reason

The ReAct loop starts with a reasoning phase that assesses the current situation. An LLM uses historical knowledge, real-time data and tools to understand what is happening and why. The ReAct agent considers organizational goals and limitations to establish a plan of action that can arrive at the desired outcome.

2. Act

The ReAct agent next decides which plan of action could achieve the desired outcome. They will often access internal data along with external tools -- such as search engines -- to gather information and interact with the real world. The ReAct agent implements the plan of action and executes the necessary steps to achieve its planned result. This produces an output that might include data, tools or real-world interactions.

3. Observe

Finally, the ReAct agent measures and evaluates the outcome of its actions compared to its goals. These observations can include feedback from human users or real-time data updates. Observation helps the ReAct agent understand the differences between intended and actual results.

Like IT administrators, ReAct agents use observation data to adjust or change a plan. It's easy to imagine that changes only take place when outcomes are undesirable, but in fact, the ReAct agent always learns from its iterations. Even successful outcomes reinforce and strengthen the agent's behaviors. This iteration enables ReAct agents to adapt and learn over time, reducing errors and refining accuracy and performance to yield optimal results.

Enterprise benefits of ReAct agents

The emphasis on LLMs and CoT reasoning has made ReAct agents powerful tools for enterprise AI. Benefits of ReAct agents include the following:

• Complex workflow automation. ReAct agents can automate complex, multi-step decision-making processes or workflows. These agents exercise a high degree of autonomy to address dynamic situations, such as technical or customer support, data analytics or research. This promises cost reduction and operational efficiency for enterprise adopters.
• Flexibility. ReAct agents can support a wide range of integrations. This enables the agent and its LLM to use many different external tools and APIs with little to no prior configuration or tuning. This makes ReAct agents well-suited for diverse and complex tasks in various enterprise settings.
• Ability to learn. ReAct agents are adept at handling dynamic and situational tasks, selecting the best tools or APIs with little tuning. This means ReAct agents can use their reasoning abilities to learn and adapt to unexpected, unplanned or challenging situations that they are unequipped to handle. The agents can learn from past outcomes, access external data sources and approach unforeseen situations with success.
• Accurate outcomes. CoT behavior carries an increased risk of hallucinations. However, the ability of the ReAct agent to access and analyze varied internal and external data sources can vastly reduce the risk of hallucinations. This kind of "sanity check" can help make ReAct agents far more accurate and dependable, which also benefits governance and compliance.
• Observable and explainable. ReAct agents and their LLMs use a CoT reasoning process that divides a prompt into manageable steps. This detailed and organized reasoning process makes ReAct agents easy to follow, debug, explain and refine. Such clarity makes the agents explainable and facilitates governance and compliance.

Limitations and challenges of ReAct agents

Despite the powerful benefits that ReAct agents can bring to an enterprise, there are also several important limitations that potential adopters should consider, including the following:

• Error potential. ReAct agents can adapt to changing circumstances and alter the execution order of tasks and workflows. This flexibility is desirable but can lead to potential problems such as infinite loops and amplified errors in the agent's activity. ReAct agents demand robust design, extensive testing, clear error handling guardrails and close validation of outcomes.
• Task complexity. ReAct agents rely on an LLM's context window for short-term memory to maintain context, conversations and task steps. At the same time, LLMs store CoT activity within the context window. Complex tasks that require many steps or retain significant volumes of information can exceed the context window. This can lead to LLM token limits, lost context and inaccurate outcomes.
• Computing limitations. ReAct agents employ large and sophisticated LLMs for effective reasoning capabilities. This requires significant computing resources and can incur computing and API costs. The iterative and multi-step behaviors of ReAct agents can also lead to latency, which slows output and decision-making. This can be problematic in real-time control loops where users desire fast and reliable responses.
• Complex integrations. Integration with existing systems and tools is crucial for ReAct and other types of AI agents. Integration of complex agentic workflows with legacy systems and tools requires additional work and testing. To combat integration challenges, developers should consider integration needs during ReAct agent design and development.
• Prompt design. ReAct agent performance is dependent on the design of the prompt, as well as the tools and workflows that parse it. This can demand significant customization of prompt mechanics and processing -- especially for vertical ReAct agents. Prompt creators, such as prompt engineers, should be well-versed in prompt creation and optimization for the specific LLM in use with the ReAct agent.
• Data security. ReAct agents access sensitive information from a wide range of sources. A robust and comprehensive security framework must safeguard this data and ensure that it is accessed in accordance with acceptable use guidelines. This can involve role-based access control, data anonymization, encryption, careful agent and tool testing, and well-considered guardrails for agent data handling and exfiltration.
• Data quality. An agent is only as effective as its data. ReAct and other AI agents depend on high-quality data that is accurate, complete, unambiguous, properly formatted and relevant. Data science expertise is necessary to ensure data quality and address potential data bias that can exclude, discriminate and result in unfair outcomes.

A guide to ReAct prompting

ReAct prompting is a prompting technique that engineers can use to enable LLMs to facilitate an effective ReAct agent. This prompting technique follows the reason, act and observe paradigm explored earlier. While ReAct agents do not require ReAct prompting, it can provide engineers with advantages like greater control over the agent and insight into the agent's CoT.

A ReAct prompt and the subsequent agent actions include the following elements:

• User input. The prompt, formatted as a statement or question, that causes the agent to begin its reason, act and observe cycle. Due to the "act" nature of ReAct agents, user input can be detailed, often including function calling, integration with tools and external knowledge bases or few-shot prompting -- a prompting technique where user input includes example outcomes for the ReAct agent to use as context.
• Reasoning. The reasoning, or thoughts, the ReAct agent displays. This can provide engineers with a detailed pathway of the agent's CoT.
• Actions. Agents perform actions with the functions, tools and examples that their engineers provide them with. Therefore, engineers must ensure their agents have an appropriate toolkit.
• Observation. This is the feedback or result the agent receives from the action step. These observations guide the next reasoning step.
• CoT loop. The reason, action and observation loop can continue as long as necessary. Engineers determine how many times the agent repeats this loop before providing an answer.
• Output and final answer. Once the agent satisfies the loop conditions, it can provide the user with a final response to their input or query.

ReAct agents: Then vs. now

ReAct agents were first postulated in a 2022 paper from Google. The paper discussed LLMs that could create a traceable reasoning path and interleave task-centric actions or outcomes with each step in the reasoning process. The goal was to advance how LLMs interact with the real world and handle complex requests or problems that pose a challenge for other types of AI agents, which separate reasoning from actions. It was this initial work that laid the foundation for the thought-action-observation paradigm.

Early adoption of the ReAct paradigm led to the development and adoption of a ReAct framework in 2023 and 2024. This framework formed the basis for emerging AI frameworks, including LangChain and AutoGPT. During this brief period of emergence, developers addressed poor search problems and mitigated prompt complexity requirements. Variations, such as zero-shot ReAct agents, also emerged to handle specific domains and use cases.

By 2025, ReAct agents improved in their ability to tune and interpret complex, nuanced prompts. At the same time, integrations diversified to support more tools and APIs -- including scientific knowledge graphs -- while ReAct principles were added to foundation models from leading AI providers such as OpenAI and Google.

Moving forward to 2026 and beyond, expect ReAct agents to offer more support for enhanced learning and self-tuning capabilities, collaboration with other AI agents to handle real-world environments (such as vision inputs and speech outputs), and superior access to external data (such as knowledge bases) to improve decision-making and reduce hallucinations.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 30 years of technical writing experience in the PC and technology industry.

Everett Bishop is the assistant site editor for SearchCloudComputing. He graduated from the University of New Haven in 2019.