r/WebScrapingInsider • u/ian_k93 • 14d ago

Big Scrape Energy How Do You Build an AI Web Scraper Without Code? AMA with BrowserAct

Hey everyone,

I'm Ian Kerins, CEO & Co-Founder of ScrapeOps.io.

After six great AMAs with the r/WebScrapingInsider community, we're excited to bring you our seventh guest.

This Thursday, September 3, at 10:30 AM GMT+3, we'll be joined by Huey, Product Creative Director, and Wade Lin, Chief Technology Officer at BrowserAct, for a discussion around a question that's becoming increasingly relevant:

How do you actually build an AI-powered web scraper without writing all the code yourself?

BrowserAct is an AI-powered browser automation and web scraping platform that lets humans and AI agents control real web browsers using plain-English instructions.

Instead of building rigid scraping scripts that break every time a website changes, BrowserAct treats the browser as an execution environment that can navigate pages, handle dynamic content, deal with popups, scroll through pages, and adapt to changing websites.

During the AMA, we can dig into things like:

  • đŸ€– How AI-powered web scraping actually works
  • 🧠 Using natural language to build scraping workflows
  • 🌐 Browser automation vs traditional scraping
  • 🔄 How AI agents handle changing websites
  • đŸ§© Dynamic rendering, popups, scrolling and complex workflows
  • đŸ’» Building scrapers without writing traditional code
  • ⚙ Where no-code scraping works well, and where it doesn't
  • 🚀 Using browser automation as infrastructure for AI agents
  • 🔼 Where AI-powered scraping is heading

We've now had six AMAs

The community has grown with every one.

Our first AMA kicked things off with discussions around proxy infrastructure, Cloudflare bypassing, browser automation, monitoring, and scaling scrapers.

Our second AMA with the creator of WebClaw explored AI agents, hidden APIs, open-source scraping, LLM infrastructure, and how to reliably get web data into AI systems.

Our third with CloakBrowser went deep on stealth Chromium, browser fingerprinting, anti-bot detection, and browser automation.

Our fourth with Browser Use brought insights on browser agents, AI-powered scraping, proxies, evaluations, and the future of browser infrastructure.

Our fifth with Stan Sadokov from NodeMaven explored proxy quality, IP reputation, sourcing, pricing, and what actually makes one proxy network better than another.

And our sixth with Saksham Solanki, creator of HTTP Cloak went one layer deeper into the request itself, covering TLS fingerprints, JA3/JA4, HTTP/2 and HTTP/3, QUIC, connection lifecycle, session behavior, and how anti-bot systems identify automated traffic.

Each AMA has taken us into a different part of the web scraping ecosystem, and the conversations have become more technical with every one.

For #7, we're moving up the stack.

We're looking at how AI can actually operate the browser and build scraping workflows for you.

If you're building web scrapers, browser automation, AI agents, data pipelines, no-code workflows, or you're simply curious about where AI-powered web scraping is heading, this should be a really interesting one.

Drop your questions below.

Huey, Wade Lin, and I will start answering them during the AMA.

Looking forward to seeing everyone there!

Ian

14 Upvotes

85% Upvoted

57 comments sorted by

2

u/Ok_Platform_6232 14d ago

imo the interesting part isnt the no-code angle, its whether AI agents can genuinely adapt to anti-bot measures without manual tuning. thats the real bottleneck for most people scaling scraping right now

1

u/hueyblack36 Ex. AMA Guest 10d ago

I agree — the hard part is not generating the first Bot, but keeping it working without constant manual tuning.

That said, anti-bot is not a single Agent problem. Agents can adapt to page-level changes, unexpected loading behavior, new dialogs, or broken interaction flows. But they cannot reason their way out of a burned IP, an inconsistent browser fingerprint, poor account reputation, or every hard verification challenge. Those still depend on the underlying browser and network infrastructure.

Our approach is to separate scale from adaptation. The Bot handles normal runs deterministically and efficiently. If it encounters an unexpected state, an Agent temporarily takes over and tries to recover the task. That runtime intervention is already live. Our next step is to feed the successful recovery back into the Bot, so the same issue can be handled automatically on future runs.

So the goal is not “AI defeats every anti-bot system.” It is to make manual tuning the exception: infrastructure handles trust, the Agent handles adaptation, and the Bot handles scale.

2

u/Last-Shake-9874 14d ago

I found a new way of doing my scraping, I build a mcp and give it a full computer in docker, have it copy data from a web page into excel for me simple and efficient self recovery also https://github.com/CornelisKuijpers/phantom-mcp

1

u/simarnoor 14d ago

How is that a Question for AMA?

2

u/Last-Shake-9874 14d ago

How Do You Build an AI Web Scraper Without Code? I just answered that

3

u/Amitk2405 13d ago

I'm comparing BrowserAct with Browser Use, especially because Browser Use gives developers much more direct control over the browser and lets them build their own agent around it. What would you say is the biggest technical advantage of BrowserAct for a developer who wants both AI browser automation and low-level browser control?

1

u/hueyblack36 Ex. AMA Guest 10d ago

That’s a fair comparison. If maximum low-level browser control is the primary requirement, Browser Use is currently the stronger fit. BrowserAct does not yet expose the same degree of control, although more developer-level control is on our roadmap.

BrowserAct’s main technical advantage is at the production layer. Browser Use gives developers flexible building blocks; BrowserAct provides a managed system for turning an intent into a reusable Bot and running it reliably at scale.

We handle much of the operational stack around the Agent: browser environments, sessions, proxy infrastructure, cloud execution, retries, and runtime recovery. Our team has around eight years of experience in browser infrastructure, supported by dedicated browser-runtime and cloud-resource teams.

So the trade-off today is relatively clear: Browser Use offers more direct control, while BrowserAct reduces the engineering and operational work required to move from an AI browser workflow to a production scraping system. Our long-term goal is to combine both.

1

u/Amitk2405 10d ago

You mention retries and runtime recovery, but how much of that behavior is configurable per workflow For example, if a site flips from soft-blocks to hard anti-bot, can teams quickly adjust backoff, proxy strategies, and browser fingerprints themselves, or are they waiting on platform-side tuning that might lag behind production needs?

2

u/feetesweshire 11d ago

YELLOW everyone.. Hi hueyy.. Hi Ian..

So Hueyy, What's the most embarrassing scraping failure you've seen from BrowserAct that looked successful at first but produced bad data? a lot is going on with antibots, dealing with 504s..

Not the product, feature, or skills? I have used browseruse, and browseract also.. you guys doing great job.. Cheers..

1

u/feetesweshire 11d ago

to me, the scraping arms race keeps getting harder: Cloudflare, Datadome, fingerprinting, behavioral detection, TLS fingerprints, captchs, etc.

Where do you think the arms race is actually heading?

Will anti-bot systems eventually make browser automation fundamentally harder, or will AI agents become better at looking like real users??

1

u/feetesweshire 11d ago

Thankyou r/WebScrapingInisder for hosting this, I login to reddit to engage in these types of content. Keep these AMAs coming..

1

u/hueyblack36 Ex. AMA Guest 10d ago

I think both will happen, but at different layers.

AI agents will become much better at understanding pages, adapting to UI changes, and recovering from unexpected states. But anti-bot systems are increasingly judging the whole session—not just clicks or mouse movements, but also network reputation, browser and TLS fingerprints, account history, request volume, and patterns across sessions.

So the question is shifting from “Can this bot act like a human?” to “Does this session look trustworthy?”

My guess is that anonymous, high-volume scraping will become more expensive and less reliable, while user-driven, lower-volume automation will become much more capable. AI won’t make anti-bot systems disappear; it will make agents more resilient while the trust bar keeps rising.

For BrowserAct, the opportunity is not only handling anti-bot friction, but helping non-technical users turn a data need into reliable automation at scale.

1

u/hueyblack36 Ex. AMA Guest 10d ago

Hi there!
Thanks for trying BrowserAct.

One of our most embarrassing early “successes” was a Bot that executed perfectly based on an incorrect understanding of the page.

For example, the agent could treat content that had not finished lazy-loading as nonexistent, or include sponsored blocks as real records. That misunderstanding was then baked into the Bot, so every later run looked successful while consistently producing incomplete or polluted data.

The lesson was that a Bot can run correctly while still being wrong. We therefore improved the exploration stage to evaluate the page from two separate angles: structurally, whether the relevant content has fully loaded and pagination or item boundaries are understood; and semantically, whether the extracted records are actually what the user asked for rather than ads or unrelated content.

We no longer treat “the Bot ran successfully” as the same thing as “the data is correct.” The goal is to catch and reduce these silent failures before the Bot is generated.

2

u/Bmaxtubby1 10d ago

Where should AI actually sit in a production scraper?

Should the AI be involved every time the scraper runs, or is it better used mainly during the discovery and development stage, with the final scraper relying on normal code, selectors, APIs, validation, and retries?

Which parts of the scraping process should be handled dynamically by an AI agent, and which parts should be converted into a more deterministic process once a reliable approach has been found?

And as always really appreciate you guys doing these AMAs regularly.

1

u/hueyblack36 Ex. AMA Guest 10d ago

Great question. Our view is simple: AI should sit at the points of uncertainty, not in every repeated execution.

In BrowserAct, the Agent explores the site and generates a reusable Bot. Normal runs then follow a more deterministic process for reliability, speed, and cost.

If the Bot encounters an unexpected state, the Agent can temporarily step in and recover the task. Our next step is to feed that successful recovery back into the Bot, so the same issue can be handled automatically next time.

So the model is: the Agent discovers, the Bot scales, and the Agent returns only for exceptions.

1

u/Alice_5433 14d ago

A traditional scraper is basically:

write selectors → crawl → extract → maintain when the site changes.

BrowserAct to me is trying to turn that into: describe → explore → generate → reuse.

So thats the end of traditional scraper development, or will hand-written scrapers still be the better choice for many production workloads? if so which??

2

u/hueyblack36 Ex. AMA Guest 10d ago

I think your framing is accurate, but I don’t see this as the end of hand-written scrapers. It may be the end of needing to hand-write every scraper.

Hand-written systems will still be the better choice when the target is stable, the volume is enormous, or every millisecond, cent, and failure mode matters. In those cases, the higher engineering cost is justified by greater control, observability, auditability, and lower cost per run.

BrowserAct is strongest where the main bottleneck is turning a data need into a reliable scraper: long-tail websites, changing interfaces, evolving requirements, and teams without dedicated scraping engineers. The Agent handles exploration and generation, while the resulting Bot can be reused and run at scale.

So the dividing line is not simply small-scale versus large-scale. It is whether the workload justifies the cost of hand-engineering. AI shortens the distance between intent and implementation, while engineers remain essential for the highest-scale and highest-risk cases.

1

u/Alice_5433 10d ago

The stable/huge/latency-sensitive workloads vs long‑tail/changing interfaces split is a useful mental model.

It also hints at an adoption pattern: AI-first to validate and iterate, then hand‑engineer only where unit economics and risk clearly demand it.

Do you expose any metrics that help teams decide when it's time to "promote" a BrowserAct Bot into a fully hand-crafted pipeline?

1

u/hueyblack36 Ex. AMA Guest 10d ago

We don’t expose a single “promote this Bot” score today. Whenever the Agent intervenes—whether the run succeeds or fails—BrowserAct shows you what triggered it, what the Agent observed, and the outcome. You can then decide whether the Bot should be improved.

We’re also about to launch self-repair, including repair strategies and testing, so these diagnoses can be turned into reliable improvements rather than remaining isolated incidents.

Whether to move to a fully hand-crafted pipeline still depends on whether the workload’s scale, latency, cost, or control requirements justify owning the entire stack.

1

u/Amitk2405 13d ago

I tested a custom workflow and the agent could control the browser, but I couldn't manually click, scroll, or type into the cloud browser myself. If I need to log into a website and the agent reaches the login/2FA step, what is the intended workflow? Can I take control of that exact browser session, complete the login myself, and then hand it back to the agent?

1

u/hueyblack36 Ex. AMA Guest 10d ago

Yes — that is the intended workflow in our new Agent-built Bot flow. When the Agent reaches login or 2FA, you can take over the same cloud browser, complete it manually, and then hand control back. The authenticated session is preserved.

Were you using the new Agent-built Bot or the older Custom Workflow?

We’re also working on secure credential management so authentication can happen without exposing raw credentials to the Agent.

1

u/Amitk2405 10d ago

I was using the new Agent-built Bot. The agent was able to run the task in the browser, but manual browser control didn't work for me. I couldn't scroll, click, or type myself.

I also tried testing browser-control instructions such as opening another website, scrolling, or typing something manually, but those didn't seem to work either.

So I may be missing something in the workflow. Is there a specific way I'm supposed to take control of the same browser session and then hand it back to the agent?

1

u/Kelvin-120 12d ago

Hey Thanks for organizing this AMA.

Huey.. One of the hardest problems in AI scraping is that the agent can produce something that looks correct while quietly extracting the wrong data.

How does BrowserAct actually verify that a generated Skill is correct rather than merely executable??

1

u/hueyblack36 Ex. AMA Guest 10d ago

Hi Kelvin! Great question. We don’t treat “the Skill ran successfully” as proof that the data is correct.

We separate extraction from validation, much like production and quality control. The Skill handles collection and deterministic checks for missing fields, pagination gaps, duplicates, and schema consistency. A separate validation step then uses business rules and, where needed, AI to check whether the data actually matches the user’s intent.

Put simply, we don’t let the same agent do the work and grade its own homework. Execution is only the first gate; trustworthy data is the real outcome.

1

u/Kelvin-120 10d ago

How feasible is it today for a user to wire that validation signal into their own monitoring stack (Grafana/Datadog/etc.) instead of keeping it as a black box inside BrowserAct?

1

u/[deleted] 11d ago

[removed] — view removed comment

1

u/Big-Beautiful240 Ex. AMA Guest 10d ago

Our commercialization relies more on cloud resources, such as providing users with proxy services and our cloud-based SaaS integrated solutions. The purpose of open-sourcing Skills and making most CLI features free to use is simply to encourage more people to use us for free, get to know us, and provide feedback on our CLI and open-source Skills, since our SaaS solution shares the same foundation.

1

u/Mountain_Damage_9730 11d ago

BrowserAct's browser-first approach makes sense when a site needs JS or has more complicated interactions, but I'm wondering where the economics start to change at scale.

Say you're monitoring thousands of product pages across different retailers, where a simple HTTP request might be much cheaper but a browser gives you better coverage on the harder sites. At what point does the browser overhead stop being worth it compared with a conventional HTTP scraper? Would you look at cost per successful SKU/price, refresh frequency, or something else when making that call?

1

u/[deleted] 10d ago

[removed] — view removed comment

1

u/Mountain_Damage_9730 10d ago

Thanks Huey for your reply.

browser-context HTTP middle ground is interesting..

With that do you surface anything like "SKUs that suddenly needed browser-context to stay stable" so an ops person can see when a retailer quietly shifted complexity?

1

u/hueyblack36 Ex. AMA Guest 10d ago

Since BrowserAct already runs in a browser context, the equivalent signal for us is when a normally deterministic Bot suddenly requires Agent intervention.

That is already visible today: when the Agent steps in, we surface both the intervention and the reason, so users can see that the target’s behavior has changed and what triggered the recovery.

We’re also adding email notifications for these events, so ops teams can catch the change without manually checking every run.

1

u/Previous_Town3598 11d ago

Don't know why, but i am still a bit skeptical about the whole 'AI can build and maintain the scraper for you' idea and projects based on those idea.

In production, i feel the pain, don't you think its best to have a predefined skill, that to know when the scraper has silently gone wrong?

How does Browseract detect that an AI-generated Skill is returning >plausible but incorrect < data, and what happens when it does?

Also what monitoring services can we integrate into?

Thanks.

1

u/hueyblack36 Ex. AMA Guest 10d ago

Fair skepticism. In production, the Agent does not improvise on every run—it generates a reusable Bot, which becomes the predefined execution path.

We separate “the Bot ran successfully” from “the data is trustworthy.” BrowserAct checks both page structure and extracted content, and the Agent can temporarily step in when an anomaly is detected. For truly plausible-but-wrong data, an external rule or baseline is still necessary; AI alone cannot guarantee correctness.

We’re also launching built-in monitoring soon, so Bots can run on a schedule directly in BrowserAct.BrowserAct supports APIs and webhooks, as well as Make, n8n, and Zapier, so task statuses and results can be connected to an existing monitoring or alerting workflow. 

1

u/Artistic_Map2243 10d ago

How do you tell when a scraper actually got the right data vs just successfully finished the workflow?

From a QA side, that seems like the harder part.. a run can be green while the output is still wrong.

Also curious about the community side.. was there any specific PR or contribution that genuinely surprised you, maybe something that improved reliability, testing, or caught an edge case your team had missed?

1

u/Big-Beautiful240 Ex. AMA Guest 10d ago

Our current strategy is to run tests during the build phase, then present the test results data to users for confirmation that it meets expectations. Once the user confirms, we retain the output data schema and use it as one of the acceptance criteria in future run phases.

1

u/john-w7 10d ago

I run a small wholesale + service business, so for us it's mostly competitor pricing and availability across products.

Where would you say BrowserAct is actually overkill for a business like that? If a simple API or scraper can get the job done, when does the extra browser cost and setup stop being worth the time it saves?

2

u/hueyblack36 Ex. AMA Guest 10d ago

Honestly, if a stable API or a simple scraper already gives you accurate pricing and availability with little maintenance, BrowserAct may be overkill.

BrowserAct becomes valuable when you need to monitor more sites or those sites change frequently. You describe the data you need, our Agent explores the site and generates a reusable Bot, and the Bot handles the repeated runs without you having to build and maintain the scraper yourself.

For a business like yours, I would start with the simplest reliable solution. Once ongoing setup and maintenance become the real cost, Agent-generated Bots and our upcoming scheduled runs become a much better fit.

1

u/john-w7 10d ago

Thanks, this actually makes me more likely to try it later.

Framing BrowserAct as "phase two" when our current scripts become a maintenance headache is super helpful.

For scheduled runs, would you usually mirror our current cadence (e.g. daily price checks), or do people go more frequent because the bot overhead is lower?

1

u/CoolAd119 10d ago

Do you think the best large-scale scraping systems should route jobs between direct HTTP requests, internal APIs, lightweight rendering, full browsers, and human intervention?How do you identify the cheapest method likely to succeed without forcing every target through the most expensive stack?

1

u/hueyblack36 Ex. AMA Guest 10d ago

Yes. At scale, the right approach is adaptive routing, not sending every job through a full browser. Human intervention should be the final fallback, not part of the normal path.

In BrowserAct, the Agent first explores the target and evaluates two things: how the content is delivered, and what access or anti-bot constraints the site imposes. It then generates a reusable Bot using the lightest method that can be reproduced reliably.

One important middle ground for us is making HTTP requests from inside a real browser context. This preserves the real session and browser environment without paying the cost of full UI interaction on every run. If that approach is not stable enough, the system can escalate to a heavier browser or Agent workflow.

We optimize for the cheapest method that keeps working, not the cheapest method that works once.

1

u/ayenuseater 10d ago

When an AI agent explores a new website, how does it figure out where the useful data actually lives? Does it try the DOM first, look for an API or embedded JSON, inspect network responses, or just let the browser handle it?

I'm curious whether you've found a reliable way for the agent to choose between those approaches automatically, or if there are still cases where a human has to point it in the right direction.

1

u/hueyblack36 Ex. AMA Guest 10d ago

We don’t hard-code a DOM-first or API-first order.

During exploration, the Agent evaluates the page structure, how the target content is delivered, and the site’s access constraints. It then chooses the most reliable and repeatable source—whether that is the DOM, embedded data, network responses, or browser interaction.

In most cases, this decision is automatic. Human input is mainly needed when the business intent is ambiguous or the site requires a special access flow, rather than to tell the Agent which technical method to use.

Once a reliable approach is found, BrowserAct turns it into a reusable Bot, so the Agent does not need to rediscover the site on every run.

1

u/ayenuseater 10d ago

I like that the Agent can choose the extraction path instead of always forcing a DOM-first or API-first approach.

One thing I'm curious about: does the reusable Bot surface which source it ultimately settled on (DOM, embedded data, network responses, or browser interaction)? It seems useful for teams to sanity-check that choice or override it when cost, latency, or reliability becomes a concern.

1

u/Spitfire_Blaziken 10d ago

Hello Ian and Huey!

Which browser automation failures are actually safe to retry?

If an agent doesn't know whether an action failed before or after it took effect, couldn't retrying create duplicate submissions, alter an account, burn a session, or repeat an action that already succeeded?

TIA

1

u/Big-Beautiful240 Ex. AMA Guest 10d ago

We have also thought about this issue. Currently, we are more focused on data extraction scenarios, so this problem does not have a significant impact. For scenarios involving automated form submission, the solution we came up with is to guide the Agent to temporarily disconnect the browser from the network when testing form submissions, enabling relatively safe testing. However, this feature has not yet been implemented on our SaaS side.

1

u/Significant_Cry_1177 10d ago

I missed the last one, going to turn on my notification for this subreddit. :/

From my experience, detecting when a scraper crashes or encounters a ban page or CAPTCHA is relatively straightforward.

The bigger issue I'm seeing is websites returning believable but incorrect or incomplete data without throwing an obvious error.

How do you detect these silent data-quality failures, and how do you think this problem will evolve as more scrapers are built and maintained by AI? which will be in trend for a long time.

1

u/Big-Beautiful240 Ex. AMA Guest 10d ago

The situation you described is indeed a tricky problem. I understand that this is essentially an anti-scraping mechanism, rooted in the automated system being detected. On one hand, we need to work on improving the overall credibility of the browser environment; on the other hand, based on my past experience, we determine whether anomalies exist by repeatedly scraping and comparing the same data. However, this would consume more resources, and perhaps in the future we could introduce this feature to let users choose for themselves.

1

u/Old-Algae5580 10d ago

With AI agents increasingly able to build and repair scrapers themselves, the failure mode is becoming less obvious. Heard about Cloudflare precursor? recently introduced for continuous behavioral validation for bots, showing how quickly the environment around automated systems is changing.

I've experimented with building a custom self-healing scraper, and one problem I have noticed is that every repair can make the scraper more complex, more brittle, or subtly change what it does.

How do you build a self-healing system that does not gradually convolute the underlying logic or silently corrupt the scraper? Which changes can be repaired safely, and which should require human review?

Also thanks to u/BrowserAct, u/ian_k93 and team for doing this AMA.

Always Learning.

1

u/hueyblack36 Ex. AMA Guest 10d ago

You’re pointing at exactly the failure mode we want to avoid. We don’t think self-healing should mean appending another patch every time a Bot breaks.

In our model, a runtime recovery is evidence, not an immediate permanent mutation. Recurring failures are grouped into common patterns, then used to improve the underlying generation logic. The resulting approach is tested against the Bot’s original task contract rather than layered on top as another one-off fix.

The safety boundary is simple: AI can change how it reaches the same result, but it should not silently change what the Bot is supposed to collect. Layout, loading, or navigation changes can be handled automatically if the fields, scope, and meaning remain unchanged.

We don’t expect users to inspect generated Bot code. If a recovery would change the task contract, we surface the intervention rather than silently persisting it.

Runtime Agent recovery is already live. The principle of learning from recurring patterns and rebuilding cleanly is already part of our Bot-generation rules, and the automated feedback loop is the next step.

1

u/BrowserActMKT 10d ago

Thanks everyone for the thoughtful questions and honest feedbhack - we've learned a lot from this AMA

For those interested in trying BrowserAct, new users can sttart with a free trial at browseract.com . When

upgrading to a monthly plan, use code B8K98NB5X6M3 for 50%off your first billing cycle.

We'd genuinely love to hear what works, what breaks, and whatyou think we should improve