Best AI Browser Agents in 2026: Compared by Use Case

Browser agents for builders fall into five categories based on their use cases: Browser infrastructure (TinyFish, Browserbase), research agents (Perplexity, You.Com), browser automation agents (Skyvern, MultiOn), developer frameworks (Browser Use, Stagehand, Playwright MCP), and enterprise automation platforms (UiPath, Automation Anywhere).

These products matter because agent adoption is moving quickly. McKinsey reports that 62% of organizations are experimenting with or deploying AI agents. Gartner predicts that 40% of enterprise applications will include task-specific agents by the end of 2026, up from fewer than 5% in 2025.

But the term AI browser now describes several different products:

• Consumer browsers with built-in assistants
• Research agents that search and synthesize sources
• Automation agents that complete web tasks
• Developer frameworks for building custom agents
• Browser and agent web layer infrastructure for production execution
• Enterprise platforms that coordinate agents, robots, and human approvals

These products should not be ranked as though they solve the same problem. This guide focuses first on the products builders can integrate into an AI-agent stack, then covers consumer, research, and enterprise alternatives for context.

TL;DR: Which AI Browser Agent Should You Choose?

• Best production web layer: TinyFish (91.1% on the WebVoyager benchmark family), a unified platform for Search, Fetch, Browser, and Web Agent. Developers can start with web search or extraction and grow into authenticated, multi-step browser workflows without stitching together multiple vendors.
• Best open-source framework: Browser Use reports 89.1% on WebVoyager; in the Mersault-run evaluation where all agents used the same model, it scored 88.3% against TinyFish's 91.1%
• Best AI browser automation agent: Skyvern, particularly for form-heavy workflows and visual browser automation.
• Best consumer AI browser: Perplexity Comet for AI-assisted browsing, research, and everyday productivity.
• Pricing models differ by platform. TinyFish bills from one credit pool across all four products. Search and Fetch are free on every plan, rate-limited by tier; Web Agent is metered per step and Browser per session minute. Pay-as-you-go credits are $0.015 each. Pricing verified July 2026.
• Browser infrastructure providers such as Browserbase, Browserless, and Steel typically price around browser sessions or browser runtime, while consumer browsers use subscriptions and open-source frameworks are generally free to use but require your own infrastructure.
• There isn’t one browser agent that's best for every workload. The right choice depends on whether you need AI-assisted browsing, research, browser automation, developer tooling, or production web infrastructure.

TL;DR: Browser Agents Stack’s Key Capabilities Summary Table

1. What Is an AI Browser Agent?

An AI browser agent is a system that can interpret a goal, inspect a website, decide which actions to take, execute those actions, and adjust when the page or workflow changes.

Instead of telling the system exactly which button to click, you describe an intended outcome.

For example: Find the shipping cost for a two-kilogram parcel from London to Berlin and return the cheapest tracked option.

A traditional automation script would need predefined selectors, page paths, and error-handling logic. A browser agent can potentially navigate the website, enter the shipment information, interpret the resulting options, and return the relevant answer.

A typical browser-agent loop looks like this:

1. Understand the user’s goal.
2. Observe the current browser state.
3. Choose the next action.
4. Click, type, scroll, navigate, or extract information.
5. Check whether the action worked.
6. Continue, retry, or change strategy.
7. Return the completed result.

This makes browser agents more adaptive than conventional browser scripts, but also more probabilistic. They can decide how to approach a task, yet that flexibility introduces questions around reliability, security, cost, and oversight.

1.1. How AI Browser Agents Differ From Other Browser Software

The boundaries between these categories are not always clean.

An AI-assisted browser may gradually add autonomous actions. A browser automation framework may add model-driven reasoning. An infrastructure provider may introduce its own agent layer. A research tool may begin completing actions as well as finding information.

That is why “browser agent” is best understood as an umbrella term rather than one narrowly defined product category.

2. Which Web Layer Does Your Browser Agent Need?

You’ll need to scope your project first, then select the smallest capable web layer.

3. AI Browser Agent Evaluation Criteria

3.1. Our Evaluation Method

We did not rank these products from feature pages alone.

When choosing an AI browser agent stack, feature pages only tell half the story. To properly compare these categories, they must be evaluated against the real architectural decisions builders face: what the platform handles natively, what infrastructure it provides, and what your engineering team still has to build themselves.

3.2. Core Production Scenarios

We framed our comparison around the actual workflows builders need to deploy in production. Any robust agent stack must account for:

• Searching and extracting information across multiple websites
• Navigating JavaScript-heavy pages, filters, and pagination
• Completing forms and multistep browser workflows
• Maintaining login state across recurring tasks
• Running concurrent workflows in production
• Debugging failures and validating the final result
• Integrating with existing models, frameworks, and applications

3.3. Evaluation Criteria

Our comparison evaluates each product category across 4 technical factors:

1. Task Completion & Performance: Whether it reaches the correct final outcome, supported by benchmark data (where available) rather than feature-page claims.

2. Developer Control & Integration: How a team interacts with the tool, including API access, framework compatibility (e.g., Playwright, MCP), and deployment options (cloud vs. self-hosted).

3. Authentication & Workflow Depth: The ability to execute beyond a single page view, including managing sessions, navigating dynamic sites, and executing authenticated multistep workflows.

4. Architectural Fit (Use Case): Whether the product is designed to be integrated as primitive infrastructure, utilized as a developer framework, or deployed as a ready-to-use research and automation platform. .

3.4. How Rankings Were Assigned

Products were ranked within their own category, not against tools designed for a different job.

A consumer AI browser was evaluated on user experience and contextual assistance. A framework was evaluated on developer control and flexibility. Browser infrastructure was judged primarily on execution reliability, sessions, authentication, observability, and scale.

Where public benchmarks were available, we included them and identified whether the result was independent or vendor-run.

4. The Best Browser Agents by Category

Comparing AI browser agents within their categories produces more useful recommendations than ranking them all against one another. The following ranking is an editorial recommendation based on category fit. Where products span multiple layers, they are placed in the category that best reflects the way they are discussed in this guide. Their broader capabilities are noted in the individual review.

4.1. Best Browser Infrastructure for AI Agents

TinyFish

Best for: AI products that need to search, fetch, navigate, authenticate, and complete multistep web workflows in production, including login and authentication.

Published benchmark evidence: TinyFish reports 89.9% success on Online-Mind2Web, a 91.1% pass rate on WebVoyager, 556 ms median Search latency, and 93% of tested pages returned as usable Fetch context. WebVoyager was independently evaluated by Meursault; the other figures are TinyFish-run evaluations on defined test sets, so they should not be treated as universal performance guarantees.

TinyFish is positioned as enterprise infrastructure for AI web agents rather than as a consumer browser or reasoning model.

Its product surface covers several levels of web interaction:

• Search for ranked search-engine results, snippets, and URLs
• Fetch for clean, model-ready content extraction
• Browser access for direct browser control
• Web Agent execution for multistep, goal-directed workflows
• Vault and Profiles for recurring authenticated workflows - Vault resolves credentials outside the model's context, Profiles replay session state." Vault alone is confusing to readers, and Profiles alone doesn't explain the security story that makes this section worth reading.

TinyFish’s Web Agent can receive a target URL and natural-language goal, then navigate, click, fill forms, log in, and return a structured result. TinyFish’s documentation recommends reusable profiles for recurring authenticated workflows.

The architectural positioning is important because it dictates how you integrate. You aren't locked into a single paradigm; three distinct adoption shapes must stay open depending on what you want your engineering team to build versus buy:

1. Primitives inside your own harness: Use individual execution layers (Search, Fetch, Browser) directly within your custom application logic.
2. Primitives as tools for another agent: Your reasoning model owns the decisions and tool-calling, passing specific execution commands to TinyFish to carry out.
3. Web Agent end-to-end: Hand the TinyFish Web Agent a high-level goal in plain English, and let it autonomously plan the steps, navigate the dynamic site, and extract the clean JSON payload.

Your reasoning model can own the decisions and call the infrastructure for execution, or you can hand the Web Agent the goal and let it plan its own steps. The primitives work inside your harness, as tools for another agent, or end-to-end.

This makes TinyFish relevant when the browser is not the end-user product, but a capability embedded inside another AI application.

Examples include:

• Competitive intelligence
• Travel and pricing data
• Ecommerce monitoring
• Marketplace operations
• Authenticated data retrieval
• Form-based workflows
• Dynamic websites without suitable APIs

TinyFish is less relevant to someone who simply wants an AI-powered personal browser. Its value appears when a team needs repeatable web execution behind a product or workflow.

How to start: Begin with the narrowest primitive that solves the problem. Use Search when ranked results and URLs are enough. Use Fetch when the task is clean page retrieval. Move to Browser or Web Agent execution when the workflow requires interaction, state, authentication, or multiple steps. Experiment your agent with TinyFish as the unified browser agent infrastructure.

Browserbase

Best for: Developers who need managed cloud browsers and a broad platform for agent web execution.

Published benchmark evidence: Browserbase reports a 5× improvement in browser infrastructure performance over the preceding year and says its platform has run more than 100 million browser sessions. Separately, its Stagehand v3 framework completed an iframe and shadow-DOM action suite more than 44% faster than v2. These are vendor-published results and measure infrastructure and version-to-version performance rather than a neutral cross-product agent benchmark.

Browserbase provides remote browsers alongside Search and Fetch APIs, agent capabilities, runtime infrastructure, and identity-related functionality.

It is particularly attractive to teams that want cloud browser infrastructure compatible with familiar frameworks such as Playwright and Stagehand.

Its strengths include:

• Managed browser sessions
• Debugging and recordings
• Framework compatibility
• Cloud deployment
• Search and retrieval primitives
• Identity and session tooling

Browserbase is best suited to teams comfortable assembling their agent architecture from platform components.

4.2. Best Browser Agents for Research

Perplexity

Best for: Fast, source-oriented web research.

Published benchmark evidence: On the independent DeepResearch Benchmark, Perplexity Deep Research scored 42.25 overall and 90.24% citation accuracy. In Perplexity’s own DRACO evaluation, it reported 89.4% in Law and 82.4% in Academic, with 459.6 seconds latency. The first result comes from an open academic benchmark; the DRACO comparison was run by Perplexity.

Perplexity is strongest when the user wants a direct answer supported by discoverable sources.

It is useful for:

• Initial market research
• Topic exploration
• Product comparisons
• Source discovery
• Rapid synthesis

Its main advantage is speed. It helps users move from a broad question to a sourced overview without manually opening every result.

Its limitation is the same as that of most research systems: a polished synthesis is not a substitute for reviewing primary evidence. High-stakes research still requires source inspection and judgment.

You.com

Best for: Users who want search, research, and AI workflow flexibility in one environment.

Published benchmark evidence: In an official methodology report, You.com recorded 77.84% SimpleQA answer accuracy with 445 ms median latency. A newer company comparison page reports 91.1% accuracy under a different configuration, so builders should check the setup before comparing the two figures.

You.com has historically combined AI search with configurable assistants and research workflows.

Its strongest use case is exploratory work where users want to move between search, synthesis, and different model-assisted tasks.

As with other research tools, teams should evaluate output quality based on their own subject area rather than relying only on generalized product claims.

4.3. Best Browser Automation Agents

Skyvern

Best for: Form-heavy browser workflows and sites where traditional selectors become brittle.

Published benchmark evidence: Skyvern reports an 85.8% WebVoyager score, meaning it completed roughly 86% of the evaluated live-site tasks under that setup. The figure is published by Skyvern, and the article does not expose complete run traces, so treat it as a vendor-reported result.

Skyvern is commonly evaluated for workflows involving visual page understanding, form completion, and interaction with browser-based systems that may not expose usable APIs.

It is most relevant when the task is operational rather than informational—for example, submitting information, retrieving a document, or completing a process across a portal.

The main evaluation question is not whether it can complete a demonstration. It is whether it can complete the team’s specific workflow repeatedly, recover from errors, and produce evidence of the final result.

MultiOn

Best for: General-purpose browser task delegation.

Published benchmark evidence: No current, reproducible MultiOn product-level score was located for WebVoyager, Online-Mind2Web, OSWorld, or a similarly inspectable benchmark. MultiOn’s research and benchmark archive now emphasizes broader on-device agents and REAL Bench, so teams should request task-success data for the exact sites they plan to automate.

MultiOn helped popularize the idea of giving an agent a natural-language task and allowing it to operate across consumer websites.

General browser-task agents can be compelling because they reduce the need to specify every step. Their challenge is consistency across a web that changes constantly and was not designed around autonomous agents.

Before adopting one, test the exact websites, account states, and edge cases involved in the intended workflow.

4.5. Best Enterprise Browser Automation Platforms

UiPath

Best for: Governed automation across agents, robots, people, desktop applications, and browser workflows.

Published benchmark evidence: No neutral, standardized browser-task benchmark was located for UiPath’s current platform. Because enterprise RPA performance depends heavily on process design, integrations, exception handling, and approvals, builders should rely on a controlled proof of concept and measure straight-through processing, exception rate, handling time, and human hours saved. UiPath’s official platform information provides capability evidence rather than a directly comparable browser-agent score.

UiPath is broader than a browser-agent product. Its platform is designed to orchestrate agents, robotic automation, and human involvement across enterprise processes.

It is suited to organizations that need:

• Approval workflows
• Auditability
• Process modeling
• Desktop and browser automation
• Integration with internal systems
• Centralized governance

UiPath may be excessive for a developer team that only needs remote browsers or an API-driven web workflow.

Automation Anywhere

Best for: Enterprise-scale process automation with AI-assisted orchestration.

Published benchmark evidence: No neutral, standardized cross-platform browser benchmark was located for Automation Anywhere’s current agent and automation stack. Its official platform information should be supplemented with a workflow-specific pilot measuring completion without intervention, exception rate, processing time, and total operating cost.

Automation Anywhere belongs in the same broad enterprise category as UiPath.

Its value is not limited to browser control. It is designed for organizations managing automated business processes across departments, applications, and data sources.

Teams should compare it based on governance, deployment requirements, integrations, operating model, and support, not only on its ability to click through a web page.

4.6. Best Browser Agent Frameworks

Browser Use

Best for: Python developers building model-driven browser agents.

Published benchmark evidence: Browser Use achieved an 88.3% task pass rate on WebVoyager in a 641-task live-web evaluation. The comparison was evaluated by Meursault and used a specific Browser Use configuration, so model choice, infrastructure, and site changes may affect results.

Browser Use provides an open-source framework for connecting language models to browser environments. It supports local or self-hosted use and also offers managed browser and agent services.

Its appeal is accessibility. Developers can move from a natural-language task to a working browser agent without constructing the entire observation-and-action loop themselves.

Browser Use is a strong choice for experimentation, prototypes, and teams that prefer Python.

The main consideration is how much of the surrounding stack the team wants to manage. Running the agent logic is only one part of operating reliable browser workflows in production.

Stagehand

Best for: Developers who want to combine deterministic browser code with natural-language actions.

Published benchmark evidence: See Browserbase’s benchmark.

Stagehand is developed by Browserbase and can run with local or other Chromium environments, but its most integrated managed deployment path is Browserbase. Stagehand offers four main primitives: act, extract, observe, and agent. It is designed to let developers use natural language where flexibility is needed while retaining code where deterministic control is more appropriate.

This hybrid model is useful because not every browser action needs an agent.

A stable login button can be handled deterministically. A dynamically structured product page may benefit from model-driven observation or extraction.

Stagehand is particularly relevant to teams already comfortable with Playwright-style browser development.

Playwright MCP

Best for: Connecting coding assistants and MCP clients to browser automation.

Published benchmark evidence: No official numerical agent-success benchmark was located for Playwright MCP itself. Playwright’s official documentation verifies support for Chromium, Firefox, and WebKit and parallel execution, but the success rate of an MCP-based agent depends on the selected model, prompts, browser environment, and workflow design.

Microsoft’s Playwright MCP server gives language models browser automation capabilities through structured accessibility snapshots. It is designed to work with MCP-compatible environments without requiring a vision model for every interaction.

It is useful for:

• Browser-based testing
• Development debugging
• Interactive website inspection
• Reproducible coding-agent workflows
• One-off browser tasks

Its strongest advantage is the relationship with Playwright’s existing automation model.

Its limitation is that it is a control interface, not a complete production browser-agent platform. Teams may still need separate systems for remote execution, authentication, scaling, monitoring, and workflow orchestration.

5. How to Choose a Browser Agent

Those above are far from the exhaustive list of AI browser agents and its infrastructure layer. To help you choose which one’s suitable for your need, here is the evaluation criteria table you can use to evaluate which browser agent to choose for your case.

The criteria that matter most change with the shape of the work, for example:

Use Case: Run Browser Workflows at Production Scale

DoorDash operates in markets where web execution feeds real operational, and data-science decisions. Its use case involves authenticated systems, dynamic websites, changing interfaces, and more than 1 million web workflows per quarter across global markets.

At this volume, small technical differences become material business differences.

A one-percentage-point increase in failures can mean thousands of interrupted workflows. A small increase in average execution time can create longer queues and higher infrastructure costs. Poor observability can make widespread failures difficult to diagnose.

For production-scale browser execution, prioritize:

• Success rate under high concurrency
• Queueing and throughput
• Session stability
• Retry and recovery behavior
• Structured errors
• Execution logs and traceability
• Regional and site coverage
• Cost per completed workflow
• Performance consistency as volume grows

Best-fit browser layer: Managed browser infrastructure or a production web-agent platform with concurrency, session management, observability, and recovery built in.

6. AI Browser Agent and Infrastructure Risks

Browser-agent failures usually appear as operational problems rather than obvious model errors. A workflow may lose authentication, get blocked by anti-bot systems, or consume far more time and compute than expected. Production teams therefore need controls around access, recovery, and execution, not only a capable agent.

Authentication and Account Risk

Authenticated browser agents may access customer records, internal portals, financial tools, or administrative settings. A mistaken or unauthorized action can therefore affect live systems rather than simply return an incorrect answer.

How it appears in production:

• A scheduled workflow may inherit broader permissions than it needs or continue operating after its intended authorization has changed.
• In one documented agent incident, a research system installed 107 unauthorized software components, overwrote a system registry, ignored an earlier refusal, and attempted an administrator-level command.
• The system had permissive access and lacked machine-enforced restrictions. Although this was not a browser-specific deployment, it demonstrates the risk of combining autonomous actions with broad account permissions.

How teams address it:

• Use dedicated, least-privilege accounts; isolate sessions by workflow; and enforce permissions outside the model’s instructions.
• Reusable authentication state can avoid repeated logins, but Playwright warns that stored state may contain cookies and headers capable of impersonating the account, so it must be treated as a sensitive credential.
• Workflows that modify shared state should also use separate accounts rather than sharing one authenticated profile across concurrent runs.

To reduce risk, modern browser-agent platforms introduce protective mechanisms at the infrastructure layer.

For example, Browser Context Profiles from TinyFish scope session state per workflow, while Vault resolves credentials at the automation layer, as a result, models see labels, not values. Combined with least-privilege accounts, this limits the blast radius of any single run

isolated browser profiles to separate sessions, credentials, and cookies by workflow. This allows teams to assign least-privilege access to each agent and significantly reduces the risk of account flags during automated runs prevents one workflow from accessing another’s data. Profiles can be reused for authenticated tasks while still maintaining strict boundaries between environments. TinyFish also supports structured logging and replay, so every action taken by an agent can be audited and reviewed.

CAPTCHAs and Bot Detection

A remote browser does not guarantee access to every website. Sites may evaluate browser fingerprints, IP reputation, request frequency, session history, and interaction patterns before allowing a workflow to continue.

How it appears in production:

• An automation may work during low-volume testing but begin receiving challenges or blocks when concurrency increases, traffic moves through a different region, or the site changes its detection rules.
• Bot-management systems must balance false positives against allowing automated abuse; Cloudflare notes that legitimate users or traffic can be repeatedly challenged or blocked when detection is too aggressive.

How teams address it:

• Test success rates on the exact target sites, account types, and regions involved. Track failures by domain and challenge type, control request rates, and provide fallback paths when access is interrupted.
• Reusing a valid session may reduce repeated authentication, but it does not eliminate CAPTCHA or policy-based blocking. No provider can guarantee universal access, so feasibility and website terms should be evaluated before deployment.

Cost and Latency

Browser tasks are usually slower and more expensive than direct API or retrieval calls because they require page rendering, session management, repeated observations, model inference, and sometimes retries.

How it appears in production:

A workflow that looks like one task to the user may involve many browser and model steps behind the scenes. In a TinyFish customer example, The Zebra reduced insurance-quote workflows that previously took minutes to execution measured in seconds, illustrating how browser latency directly affects a customer-facing product.

How teams address it:

• Start with the smallest capable layer: an API for structured access, Search for discovery, Fetch for known-page retrieval, deterministic automation for stable actions, and a Web Agent only where interpretation is required.
• Reuse authenticated state instead of logging in for every run, run independent tasks in parallel, and cache repeated actions when the page remains stable.

Teams should also impose step, retry, runtime, and spending limits. The most meaningful metric is not cost per browser minute or model call, but:

Cost and time per correctly completed, verifiable workflow.

Browser agents are improving rapidly, but they remain probabilistic systems operating on an unpredictable web.

7. Building Your AI Browser Agent Stack

We’ve broken down the five main categories of AI browser agents, evaluating them by the infrastructure they provide and what they require your team to build. Choosing the right stack ultimately comes down to your preferred adoption shape: primitive execution layers inside your own harness, tools for an existing reasoning model, or a fully autonomous end-to-end web agent.

As TinyFish engineer Uttam Bharadwaj recently pointed out, the overwhelming majority of the valuable web sits behind a login, and treating agents as first-class users across this gated web is an infrastructure challenge, not a courtesy. As this technology matures, engineering teams should expect the core friction of browser automation to shift away from prompt engineering and squarely into managing the infrastructure required to handle session state, credentials, and anti-bot evasion natively.

To quickly align your team's bandwidth with the right architectural approach, revisit the decision table in Section 5.

Start Building Your Agent With TinyFish primitives:

• Sign up: agent.tinyfish.ai
• Docs: docs.tinyfish.ai
• Cookbook: github.com/tinyfish-io/tinyfish-cookbook

8. Frequently Asked Questions

What is a browser agent?

A browser agent is an AI system that can interpret a goal and interact with websites to achieve it. It may navigate pages, click elements, enter information, extract data, and adjust its actions based on what happens.

What is the difference between an AI browser and an AI browser agent?

An AI browser is usually a complete browser product with AI features built into the user experience. Whilst a browser agent is the software capability that reasons about and performs browser actions. It may run inside a consumer browser, through a developer framework, or on remote browser infrastructure.

What is the difference between browser automation and a browser agent?

Traditional browser automation relies on rigid, pre-coded scripts that break when UI changes, while a browser agent dynamically perceives and adapts to the page

What is the best browser agent for developers?

Browser Use is a strong starting point for Python developers.

Stagehand is well suited to developers who want a hybrid of natural-language actions and deterministic code.

Playwright MCP is useful when browser control needs to be exposed to an MCP-compatible coding assistant.

Production applications may also need separate browser infrastructure.

What is the best browser agent for research?

Perplexity is strong for fast, source-oriented research.

Manus is better suited to longer tasks that need to become finished deliverables.

Genspark is useful when research flows directly into documents, slides, spreadsheets, or creative work.

What is the best browser agent for authenticated websites?

For recurring authenticated workflows, look for infrastructure that supports persistent sessions, browser profiles, secure credentials, logging, and human intervention.

TinyFish and Browserbase both provide capabilities relevant to authenticated web execution.

Can browser agents replace Playwright?

Not completely.

Playwright remains useful for deterministic browser automation, testing, and direct control.

Browser agents can sit above Playwright or similar tools to handle tasks where the correct next step depends on interpreting the page.

The strongest systems often combine deterministic browser control with agentic reasoning.

Can browser agents work on websites without APIs?

Yes. This is one of their most important use cases.

A browser agent can potentially interact with the same interface a human uses.

However, the absence of an API does not guarantee that browser automation is reliable, permitted, or economical.

Are browser agents safe?

They can be used safely only when the workflow includes appropriate controls.

Important safeguards include restricted credentials, domain allow lists, sandboxing, audit logs, approval gates, and final-state validation.

How much does a browser agent cost?

Costs vary by product category.

Consumer browsers commonly use subscriptions.

Developer tools may charge for model use, agent tasks, browser time, concurrency, or data transfer.

Open-source tools may not have licensing fees, but still require models, hosting, maintenance, proxies, monitoring, and engineering time.

Enterprise platforms typically use negotiated contracts.

Do browser agents work with CAPTCHAs?

Some platforms advertise CAPTCHA handling; others, TinyFish included, treat a served CAPTCHA as a hard stop and instead reduce how often one is triggered through stealth profiles and proxy routing. No tool can guarantee access to every site Some platforms provide CAPTCHA-handling capabilities, but no tool can guarantee access to every website.

Success depends on the site, challenge type, account, network, automation method, and applicable site policies.

What is browser infrastructure for AI agents?

Browser infrastructure is the execution layer that provides browsers, sessions, networking, state, scaling, debugging, and related operational capabilities for an AI agent.

It allows product teams to focus on the workflow rather than maintaining browser environments themselves.