What Is an Agent Loop?

Scrapeless Agent Browser provides a cloud browser for web automation and AI agents.

An agent loop is the repeated process in which an AI agent observes its current state, chooses an action, receives a result, and decides what to do next. The loop connects model decisions to external tools and continues until the task is complete or a stopping condition applies.

A model response alone does not complete a real-world task. A browser can navigate to the wrong page, an extraction can return an empty field, and a write can fail after the model has already described success. A useful agent loop carries those observations back into the next decision.

How the Observe–Act Cycle Works

An agent loop alternates between deciding what to do and checking the environment after the action. A typical iteration begins with the user’s goal, the accumulated task state, and descriptions of the tools the agent may use. The model can select a tool or return a final answer. The application executes any selected tool and adds its result to the state available to the next iteration.

The distinction between model selection and application execution matters. The model may request a browser navigation, but the browser performs it. The controller decides whether the requested URL and action are permitted, validates arguments, and records the outcome. Tool availability and authorization are properties of the surrounding application, rather than promises the model can grant itself.

The workflow and agent architecture distinction is useful here: fixed workflows follow predetermined paths, while agents choose their paths dynamically. Both can contain loops. The relevant question is who selects the next action and what evidence constrains that selection.

What an Agent Must Keep in State

Agent state should preserve enough information to make the next action correct and the final result auditable. For a research task, that includes the question, relevant sources, extracted claims, unresolved gaps, and the conditions for completion. For a form-filling task, it also includes which fields have been entered and whether submission has actually been confirmed.

Separate observations from interpretations. “The page contains a confirmation number” is an observation. “The booking is complete” is an interpretation that may require checking dates, the traveler, and payment status. Store the observation with its source so the interpretation can be reviewed. A summary without provenance can gradually turn an uncertain guess into an apparent fact.

State also needs freshness. A browser result from an earlier page should not silently describe the current page after navigation. A session identifier helps connect related browser actions, but the application still needs to associate each result with the action that produced it. A memory summary is useful context; it is not proof that the external system still has the same state.

A Worked Research Scenario

A bounded product-research loop can collect public information and stop when the requested evidence is complete. Consider an illustrative task: compare the published support policies for a small set of products, then return a source-linked summary. This is a design example, rather than a report of an executed collection.

1. The controller records the product list and required fields before browsing.
2. The agent locates the official support page for the first product and opens it.
3. The browser returns page content and its URL; the agent checks whether the page actually describes the requested policy.
4. The agent records supported claims and marks absent information as unknown.
5. The loop visits the remaining official pages and checks the completion list before writing the summary.

If one page is only a search result snippet, the task remains incomplete for that product. If a page requires authorization the task does not have, the result should record that limitation. The loop should not substitute a remembered policy or claim that navigation alone verified the information.

Source verification in agentic web scraping fits this design because retrieval and evidence acceptance are separate operations. A browser observation supplies material for the answer; the agent still needs to check relevance, scope, and whether the material supports the claim.

How Agent Loops Differ From Related Patterns

An agent loop describes execution control, while a prompt chain, retrieval pipeline, and reasoning pattern describe different parts of the system. They can be combined without becoming interchangeable terms.

The ReAct research on reasoning and acting describes a pattern that interleaves reasoning with actions and observations. An application can use that idea inside an agent loop, but it still needs independent controls for tool execution, task state, and termination. A reasoning label does not supply those controls automatically.

Stopping Conditions That Describe Real Outcomes

A stopping condition should explain why the agent is allowed to finish. Completion is one reason; a resource limit, unresolved access requirement, or request for human judgment is another. These endings should produce different result states so downstream systems do not treat every termination as success.

Define the completion contract before execution. For research, require each requested item to have evidence or an explicit gap. For a file transformation, require the output file to exist and pass relevant validation. For an external write, require confirmation from the system that owns the record. The model’s closing sentence should summarize these checks rather than replace them.

Set task-appropriate limits on elapsed time, tool actions, and spending. Also detect lack of progress: repeatedly opening the same page without learning anything should not consume the entire run. The correct stopping response may be a partial result that clearly identifies missing evidence and the next dependency.

Tool Boundaries and Untrusted Content

Tool boundaries should prevent retrieved text from changing what the agent is authorized to do. A web page can contain instructions aimed at the agent, including requests to disclose secrets or execute unrelated actions. That text is evidence about the page, not a new user command.

The OWASP guidance on LLM application risks provides a starting point for reviewing prompt injection and excessive agency. Practical controls include limiting tool permissions, validating destinations, and requiring the controller to enforce action policy. A prompt warning alone should not be the only barrier around sensitive operations.

Return only the tool data needed for the task. A research agent rarely needs an entire account history when it is checking one public policy page. Smaller results reduce irrelevant context and make evidence easier to inspect. Keep credentials in the execution environment rather than inserting them into model-visible logs.

Where Agent Browser Fits

Scrapeless Agent Browser supplies browser execution that an agent loop can use as a tool. The Agent Browser documentation describes the browser surface, while the application remains responsible for the goal, action selection, state, and final acceptance rules.

Choose a browser when the task needs rendered pages or interactions. A predictable transformation over data already in memory may need no browser at all. Review Agent Browser alongside the workload and Scrapeless pricing; browser infrastructure should be selected for the actions the loop actually needs.

Conclusion

A reliable agent loop connects each decision to an observed result and stops against a clear completion contract. Start with a narrow task, preserve evidence in state, enforce tool permissions in the controller, and distinguish completion from a bounded stop. Those choices make the final answer easier to trust and a failed run easier to diagnose.