What Is a ReAct Agent?

Scrapeless MCP Server exposes Scrapeless web capabilities to AI clients.

A ReAct agent is a language-model agent that interleaves reasoning with actions and uses observations from those actions to guide later decisions. ReAct abbreviates reasoning and acting. Its defining feature is the feedback loop: the agent can inspect information from the environment before choosing what to do next.

For a developer, ReAct is a decision pattern rather than a guarantee of sound reasoning. A model can select an irrelevant tool or misread a valid result. Build the loop around observable actions, well-defined tool outputs, and explicit conditions for stopping or reporting that the task remains incomplete.

How the ReAct Loop Works

A ReAct loop alternates between deciding what information or action is needed, invoking an available tool, and incorporating the returned observation. The next decision uses the updated task state. When the evidence is sufficient or a stopping condition is reached, the agent returns an answer or an explicit incomplete result.

The original ReAct research combines reasoning traces with task-specific actions so that models can interact with external information and environments. That is a narrower claim than saying a ReAct agent understands every tool response correctly. The application still needs checks at the boundaries between decisions, execution, and interpretation.

In a hypothetical documentation task, an agent first identifies which configuration page matters, opens that page, and checks the relevant section. If the section does not answer the question, the next action can inspect another source. The final answer should depend on inspected evidence rather than on the initial plan alone.

Reasoning Text Is Not Execution Evidence

A model's explanation of an intended action does not establish that the action occurred. The runtime must invoke the tool and capture its result. Likewise, a statement that a page was checked is weaker than an observation containing the relevant page content and a source identifier.

Keep a concise decision summary where useful, but make the action trace the operational record. Store the tool identity, validated arguments, result status, and relevant observation. You do not need to expose private model reasoning to verify whether a browser opened a page or whether a returned passage supports a claim.

A useful review asks whether the selected action addressed an unresolved requirement. If the agent already has sufficient evidence, another tool call may add cost without improving the answer. If the evidence is incomplete, a fluent conclusion should not end the task. Both cases require a completion rule grounded in the user's request.

Define the Tool Contract Before the Loop

A ReAct tool contract describes what the agent can request, which arguments are valid, what the tool returns, and which effects it can have. A read-only search tool and a publishing tool should not have interchangeable descriptions or permissions. The model should receive enough information to select between them accurately.

Validate arguments outside the model. If a tool accepts a bounded source set, enforce that set in application code. If an operation changes external state, check the required authority before execution. A model-generated explanation that an action is necessary is not itself authorization to perform it.

The Scrapeless MCP interface supplies access to web capabilities. Your ReAct implementation remains responsible for choosing appropriate tools and interpreting their observations. Avoid assuming an interface automatically supplies the surrounding task policy, evidence rubric, or stopping behavior.

Build Observations That Preserve Meaning

An observation should contain enough information for the next decision without burying the relevant signal in unrelated output. For a source inspection, useful fields include the source identifier, relevant content, retrieval status, and any limits on what was observed. Preserve distinctions such as a search snippet versus inspected page text.

A source can be relevant without supporting the requested conclusion. A product page may discuss a feature but omit its availability or conditions. The agent should record the omission instead of turning the page title into evidence. Clear observation formats make it easier to separate what the source says from what the model infers.

The agentic retrieval workflow shows why source collection and answer construction need separate contracts. In a ReAct loop, every new observation should update a specific part of the evidence state. If the state does not change, inspect whether the agent is repeating work or selecting actions that cannot resolve the remaining uncertainty.

ReAct, Fixed Workflows, and Retrieval

ReAct lets the model decide later actions from observations, while a fixed workflow defines the sequence in advance. A fixed pipeline can be easier to validate when every request needs the same stages. ReAct is useful when the appropriate next step depends on what a tool returns.

Retrieval-augmented generation is related but distinct. The original retrieval-augmented generation architecture combines generation with retrieved information. Retrieval can be part of a ReAct agent, but a system can retrieve evidence without a model-controlled loop. Describe the actual control flow instead of using the terms interchangeably.

Reflection is another separate idea. Research on language-agent feedback and reflection studies how feedback can be retained to influence subsequent behavior. An ordinary ReAct loop need not implement that memory mechanism. Add it only when you have a reason and can evaluate whether retained feedback improves the task.

Set Stopping Rules That the Runtime Enforces

A ReAct agent needs stopping rules for success, insufficient evidence, and resource limits. Define what must be true before the task is considered complete. For a source-backed answer, this might require coverage of the requested claims and inspected support for each one.

Set limits on steps, elapsed time, and permitted consumption according to the application. Enforce those limits in the runtime rather than relying entirely on a prompt. If a limit is reached, return the completed work and the unresolved requirement clearly. A resource stop should not be reported as a successful answer merely because the agent produced final text.

Detect repeated actions that do not add information. The runtime can compare normalized tool requests and evidence changes to identify stalled progress. Give the agent a defined way to report that the available tools cannot answer the question. This is often more useful than forcing another action when no valid next step exists.

Test Decisions as Well as Final Answers

ReAct evaluation should inspect whether the agent chose appropriate actions, understood observations, and stopped under the right conditions. A correct final answer can hide unnecessary tool use or unsupported intermediate assumptions. An incorrect final answer can originate in the tool layer, the observation format, or the model's interpretation.

Create evaluation cases with missing fields, conflicting pages, empty search results, and a tool result that is relevant but insufficient. Check whether the agent preserves these distinctions. A completion rubric should reward a clear statement of uncertainty when the evidence cannot establish the answer.

For browser tasks, include interactions whose visible state changes. Verify that the agent reads the updated page before making a claim about the outcome. The Agent Browser product supplies the browser surface; correctness of the model's conclusion remains an application-level concern. Track browser work and model inference separately when reviewing service costs.

Keep Retrieved Instructions Outside the Control Policy

External content can contain instructions aimed at an AI agent, so a ReAct system should preserve the boundary between observations and authorized task instructions. A web page that asks the agent to reveal secrets or change tools should be treated as page content, not as a new command from the user.

Restrict which observations can influence privileged decisions. Keep credentials outside model-visible page text, scope tool permissions, and validate external actions at execution time. These controls remain necessary even when the model is instructed to ignore malicious content. Prompt wording can describe policy; runtime checks enforce the boundaries.

When investigating a failure, inspect the precise point where source content affected a decision. A trace that retains tool results and action arguments can show whether the agent misread the source, accepted an instruction from it, or called an inappropriate tool. That evidence supports a targeted fix instead of a broad prompt rewrite.

The Takeaway

A ReAct agent chooses actions using observations from earlier actions. The useful unit is the complete decision-and-execution loop, with observable tool results and enforceable limits. Start with clear tool contracts, define what evidence completes the task, and test whether the agent stops honestly when that evidence is unavailable.