Agent Browser / Computer-use & VLA Agents

Connect visual agents to an observable web environment

Use browser screenshots and actions as the observation loop for computer-use and vision-language-action experiments on websites.

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  • Capture the current browser state
  • Let your model select an allowed action
  • Observe the result and evaluate task completion

CDP

Connect your browser framework

Sessions

Keep a task in one context

JavaScript

Interact with rendered pages

Evidence

Verify the resulting state

Who uses Computer-use & VLA Agents

Use browser screenshots and actions as the observation loop for computer-use and vision-language-action experiments on websites.

01

Agent researchers

Define the business question and review the collected evidence.

02

Evaluation teams

Use a repeatable process to compare the observations that matter.

03

AI application builders

Connect reliable inputs to the product or research workflow.

04

Integration owners

Map the source output into your existing application and validate required fields.

05

Workflow operators

Monitor exceptions, maintain collection context and review incomplete runs.

Why this workflow breaks at scale

Design around the failure modes that affect a usable business outcome.

Failure 01

The context gets lost

A screenshot can become stale between observation and action. Refresh the state and verify the effect of each important interaction.

Keep comparable observations
Failure 02

A run ends without a usable result

An action can execute without completing the intended task. Validate the visible result before treating the run as successful.

Validate the actual outcome
Failure 03

The next team cannot verify the result

Preserve source context and reviewable evidence alongside the result. Define who handles incomplete records before production collection.

Make the handoff traceable

The data your workflow can use

Keep the collection layer and the normalized business record clearly defined.

observation_id
Stable observation id assigned or retained by your application.
action
Browser action chosen by your task runner.
next_state
Outcome of the named application-level check; distinguish failure from valid empty data.
task_result
Outcome of the named application-level check; distinguish failure from valid empty data.

NORMALIZED RECORD · ILLUSTRATIVE

{
  "observation_id": "observation_sample_01",
  "action": "open_documentation_link",
  "next_state": "verified_in_sample",
  "task_result": "verified_in_sample"
}

Suggested downstream record, not a literal Scrapeless API response. Field extraction and normalization belong in your application.

A remote browser session that your automation or agent framework controls.

Example in practice. Evaluate a visual agent finding a policy document through a website menu.

Read the official product reference

How this workflow runs

A focused sequence from approved input to a verified result.

  1. 01

    Capture the current browser state

    Start with: A browser task, observation policy and action budget. Define the minimum usable result before collection.

  2. 02

    Let your model select an allowed action

    A remote browser session that your automation or agent framework controls. Record failures and partial results separately.

  3. 03

    Observe the result and evaluate task completion

    Verified task completions, exception rate and runtime per usable result. Retain evidence for records that enter the next stage.

Built for the way your team works

Product capabilities support the workflow; your application defines what success means.

  • Playwright · Puppeteer

    Connect the tools you use

    Agent Browser exposes a browser connection for frameworks such as Playwright and Puppeteer. Your existing workflow decides what the browser does.

  • Task → session → result

    Control the session

    Set the session lifetime and relevant network context. Design recovery around the lifetime of the browser and the state of the task.

  • Observe → act → check

    Work with rendered pages

    Use your framework to wait for content, interact with the UI and inspect the DOM. Validate meaningful states instead of relying on fixed delays.

  • Run log · page state

    Retain useful evidence

    Enable supported session recording when needed, and capture application-specific evidence through your code. Apply your retention policy to recorded content.

Plan the cost of your workflow

Estimate browser runtime and proxy traffic for representative tasks. Check current account terms before setting a production budget.

Pilot

Validate one workflow

Run a representative source sample and inspect both usable results and exceptions. Agree on acceptance criteria before expanding.

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Production

Measure real consumption

Verified task completions, exception rate and runtime per usable result.

Review pricing

Scale

Scope the next stage

Bring your workload mix, operating window and observed consumption to discuss the setup that fits your requirements.

These are workload stages, not named subscription plans. Current rates and account limits apply.

Choose how to run the workflow

Compare responsibilities and operating effort using your own representative workload.

Workflow responsibility

With Agent Browser

Self-managed stack

Manual process

Execution

Managed browser execution

Your team runs and maintains the execution layer.

An operator repeats each collection or browser step.

Business rules

Task planning, selectors, approval rules, extraction logic and outcome validation.

Implemented in your application.

Applied by the researcher or operator.

Acceptance

Keep outcome and content checks in your workflow.

Build validation and exception handling.

Review the captured evidence.

Cost planning

Measure product consumption plus downstream processing.

Measure infrastructure and maintenance effort.

Account for time spent per completed task.

Connect it to the stack you already run

Keep business logic and analysis close to the systems that consume the result.

  • Playwright

    Cross-browser Automation

  • Puppeteer

    Browser Control & Scripting

  • Your agent framework

    Custom Agent Integration

Task planning, selectors, approval rules, extraction logic and outcome validation. Open the product documentation for current setup and supported options.

Explore more Agent Browser workflows

Related workflows using the same product, each with its own inputs and acceptance criteria.

Production AI Agents

Connect your agent to a managed browser, turn a task into observable actions, and check the result before moving on.

Forms & Appointments

Fill approved forms, validate required fields, and retain the confirmation that proves an appointment or request was accepted.

B2B Workflow Automation

Connect browser steps across approved business portals and keep a record of each handoff from input to completed task.

Automated QA & Regression

Run repeatable checks against your application, inspect rendered page state, and capture evidence when a release changes a critical flow.

You are here

Computer-use & VLA Agents

Use browser screenshots and actions as the observation loop for computer-use and vision-language-action experiments on websites.

Public Lead Research

Navigate public company websites and dynamic directories to assemble company-level research with traceable sources for review.

Web Automation

Run navigation, clicks and page checks in a managed browser while keeping your existing automation logic and completion rules.

Owned Account Operations

Automate approved maintenance in your own business accounts with separate session context and explicit checks around account changes.

Dynamic Website Data

Render JavaScript pages, navigate interactive elements, and extract visible public data after the relevant content is ready.

RPA Modernization

Replace machine-bound website routines with managed browser sessions, explicit state checks and versioned automation workflows.

Looking for more Agent Browser workflows?

Explore more use cases across web automation, QA testing, and business operations.

All Agent Browser workflows

Questions teams ask before they build

Scope the first run with clear expectations.

What do we need to get started?

A browser task, observation policy and action budget. Begin with a representative sample and agree on the acceptance criteria before increasing volume.

What does Agent Browser provide?

A remote browser session that your automation or agent framework controls. Observed page state, extracted content and evidence captured by your automation.

Which parts do we build ourselves?

Task planning, selectors, approval rules, extraction logic and outcome validation.

What can make this workflow misleading?

A screenshot can become stale between observation and action. Refresh the state and verify the effect of each important interaction.

What boundaries should we plan around?

This is a browser environment, not general desktop or operating-system control. The VLA model and evaluation harness are external.

How should we evaluate a pilot?

Verified task completions, exception rate and runtime per usable result. Review a sample of accepted records and exceptions before deciding whether to expand.

Put Agent Browser to work for your team

Bring your sources, expected browser runs and acceptance criteria. Start with a focused pilot, then expand on evidence.

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