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We frequently get asked: “How is Intuned different from X?” This document answers those questions. This document has two sections. First, Intuned as a platform covers how we compare to other browser automation solutions. Then Intuned Agent covers how our AI coding agent compares to others.

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Intuned as a platform

This section covers how Intuned as a platform compares to other browser automation solutions—and in some cases, how they fit together.

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Browser-as-a-service vs. Intuned

Examples: Browserbase, Browserless These services expose managed browser instances via CDP (Chrome DevTools Protocol). You write automation code that runs on your own infrastructure and connects to their browsers remotely. With Intuned, you deploy a Project—a codebase containing your automation logic split into APIs. Your code runs alongside a browser with built-in stealth mode and captcha solving. You call those APIs to execute automations, and the platform handles everything else: scheduling, retries, authentication, and result delivery.

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Key differences

Performance. With browser-as-a-service, CDP traffic travels over the network. CDP wasn’t designed for this. Playwright generates heavy round trips for auto-wait, element selectors, and assertions. What runs in milliseconds locally can take seconds remotely. Moving assets like screenshots, downloads, and images adds more overhead. With Intuned, your code and browser share the same machine. No network latency, no slow asset transfers. What’s included. Browser-as-a-service gives you a browser, but you still need to run your code somewhere, handle scheduling, manage authentication, and store results. With Intuned, everything lives in one platform—one deployment, one cost. Observability. Browser-as-a-service gives you browser recordings. Intuned gives you that plus structured tracing tied to your automation logic—every Run tracks API parameters, outputs, timing, and browser traces together. You debug at the automation level, not just the browser level.
Learn more about Observability, monitoring, and logs.

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AI automation libraries vs. Intuned

Examples: Skyvern, Browser Use, Stagehand These range from open-source libraries you integrate into your code (Browser Use, Stagehand) to managed services (Skyvern, Browser Use). They share one thing: AI controls the browser at runtime. Instead of writing selectors and navigation logic, you describe what you want and the AI figures out how to do it. Intuned provides the infrastructure to run any browser automation. You can use Browser Use or Stagehand on Intuned, write Playwright code, or mix approaches in the same project. For one-off tasks, AI-driven automation can work well. For repetitive automation—scrapers that run daily, browser-based RPA workflows that execute thousands of times—deterministic code is more reliable and cost-effective. AI can still help you write and maintain that code, but it doesn’t need to run at execution time.

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Why deterministic code for repetitive tasks

Here’s what makes AI-driven automation less suited for repetitive work: Cost. Many actions require LLM calls. While some libraries offer caching, it’s limited and not effective in many cases. For a scraper that runs daily on thousands of pages, costs add up fast. Deterministic code has no per-action overhead. Speed. AI solutions rebuild the HTML tree after each action to check if the goal is reached. Even simple tasks take longer than deterministic code. Consistency. The same prompt can produce different behavior across runs. When you need automation that works the same way every time, unpredictability is a problem. Debuggability. With deterministic code, you know exactly what ran and why. With AI-driven code, decisions are made by the LLM—when something fails, it’s harder to understand what happened or reproduce the issue. Control. AI agents typically expose 8–10 high-level actions: click, type, scroll, select, upload file, hover, press key. Sometimes you need more:
  • Network control: Intercept requests, block resources, capture API responses, mock data
  • JavaScript execution: Query the DOM directly, access JS variables, trigger custom events
  • Timing and synchronization: Wait for specific conditions, network idle, or race multiple events
  • State management: Manipulate cookies, localStorage, sessionStorage
  • File operations: Handle downloads to specific paths, process files mid-automation
  • Error handling: Custom retry strategies, conditional branching on failures
  • Performance optimization: Block images, fonts, or analytics to speed up automation
  • Non-standard elements: Custom HTML components don’t work with pre-written AI skills

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API-based web extraction vs. Intuned

Examples: Firecrawl API-based solutions provide web extraction as APIs (crawl, scrape, etc.). You call the API, you get data extracted from one or more websites. These solutions focus on scraping and extracting data. With Intuned, you deploy a Project—a codebase containing any logic split into APIs. You execute your code via Runs or Jobs, triggered through the UI/API. You control how the execution works via code, what libraries to use, and how to handle edge cases.

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Key differences

Ease of use vs. control. API-based solutions give you a ready-to-use API. No need to understand how browser automation works, what libraries to use, or how to write extraction logic. You just call the API and get results. The trade-off is less control—you can’t customize how things work under the hood. With Intuned, you write code that handles any situation that fits your use case. Flexibility. With Intuned, you decide how extraction works. Want to crawl only specific types of pages based on custom logic? You control the crawl logic. Want to use a specific model or prompt for AI extraction? You choose. Want to use a particular library or pattern for extracting markdown? It’s your code. API-based solutions don’t offer this level of customization. Authentication. API-based solutions typically don’t handle login flows or session management. Intuned has built-in AuthSession support for authenticated extractions. Pricing. API-based solutions charge per API call or per page. For large-scale extraction, costs grow linearly with volume. Intuned charges for runtime—the time your automation runs. This tends to be more cost-effective at scale.

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When API-based extraction makes sense

API-based extraction works well when you don’t want to deal with the details of how extraction works and you don’t need customization or flexibility. It’s a good fit for simple, public pages where you need quick results without writing code. As usage grows and you start targeting specific types of websites, customization and flexibility matter more. That’s when companies tend to build their own solutions—more customized, more cost-effective, and tailored to their needs. That’s where Intuned fits.
Intuned has a starter project with scrape, crawl, and other APIs similar to Firecrawl. You can use it directly or customize it.

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Computer use models vs. Intuned

Examples: Claude Computer Use, OpenAI Operator, Gemini Computer Use These models from OpenAI, Anthropic, Google, and other labs let you control a browser or entire desktop through AI. They take screenshots, reason about what they see, and issue mouse and keyboard commands. These labs provide model access, but you still need infrastructure to run the browser, manage execution, handle retries, and store results. Intuned provides that infrastructure. You can use computer use models on Intuned if they fit your use case, just like you can use Playwright, Browser Use, or Stagehand.
See our computer use examples in Python and TypeScript.

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Key differences

Same AI trade-offs. Like the AI automation libraries above, computer use models control the browser with AI at runtime. This means higher cost, slower speed, less consistency, and harder debugging. See Why deterministic code for repetitive tasks for details. Control. Computer use models see the screen as pixels, not structured HTML. This means no direct DOM access, no network interception, and no fine-grained control over page state—only high-level actions (click, type, scroll) without the granular control that Playwright provides.

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When computer use models make sense

Computer use models work well for one-off tasks. For repetitive automation—scrapers that run on a schedule, browser-based RPA workflows that execute thousands of times—deterministic code is more reliable and cost-effective. AI can still help you write and maintain that code, but it doesn’t need to run at execution time. That’s the approach we take with Intuned Agent.

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Intuned Agent

This section covers how Intuned Agent—an autonomous AI agent that generates deterministic Playwright code—compares to other AI coding tools.

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General coding agents vs. Intuned Agent

Examples: Devin, Claude Code, Codex These are general-purpose coding agents. They excel at writing code across many domains, but browser automation has unique requirements they weren’t designed for. The core problem: they can’t see the page during development. Agents like Devin write JavaScript and execute it via page.evaluate(), reading back results but never seeing actual page state. Tools like Playwright MCP let them click and navigate, but these were built for runtime browser control—not for generating maintainable automation code. The agent guesses at selectors and relies on you to describe failures. Intuned Agent was built specifically for browser automation codegen. It controls a real browser during development, sees actual DOM state, and iterates until the automation works.

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Key differences

Development loop. With general agents: you prompt, agent writes code, you run it, it fails, you describe the failure, agent tries again. Slow and frustrating. Intuned Agent runs the code itself, sees failures, and fixes them autonomously. Validation at scale. Proper validation—checking pagination, edge cases, dynamic content—can take 30–60 minutes. General agents time out or lose context. Intuned Agent runs as a background task and validates against live pages until it works. Maintenance. When selectors break, point Intuned Agent at a failed Run. It sees what changed and fixes the code. General agents need you to explain what broke.

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Director.ai vs. Intuned Agent

Director generates Stagehand scripts—automation code that uses AI to control the browser at runtime. Intuned Agent generates deterministic Playwright code instead. The difference comes down to what runs at execution time. Stagehand scripts inherit all the trade-offs covered in Why deterministic code for repetitive tasks: higher cost, slower speed, less consistency, and harder debugging. For scrapers that run on a schedule or browser-based RPA workflows that execute thousands of times, deterministic code is more reliable and cost-effective. That’s why Intuned Agent outputs Playwright—AI helps you build, but doesn’t run at execution time.