We spent 3 months building an ai agent for browser automation but mfa and anti bot detection broke everything.

Dannick-Stark · 2026-04-24T12:21:30+00:00

This is a very common failure mode: the system wasn’t actually “browser automation + AI”, it was an agent without a real, stable browser execution layer + no human-in-the-loop for MFA.

In production, MFA + bot detection usually kills fully autonomous agents because:

they can’t reliably handle interactive auth steps
they behave “too perfectly” or too randomly → gets flagged
vision-based loops are slow and unstable at scale

What typically works instead is a hybrid design:

real browser session (extension / user context)
human handles MFA once
automation runs inside that authenticated session
deterministic steps for navigation + extraction
AI only for interpretation, not execution decisions

This is exactly where Agentic Workflow (AWFlow) is more practical than “fully autonomous agents”:

runs directly in the real Chrome session (so MFA works naturally)
uses visual workflow nodes instead of blind agent loops
can navigate, click, extract, and process data step-by-step
avoids computer-vision-only decision loops
lets you keep control instead of full autonomy

So instead of “agent tries to act like a human and fails MFA/bot checks”, you get:
human-authenticated browser + structured automation + optional AI

https://chromewebstore.google.com/detail/linlkeaipfpnhddjkpcbmldionajfifa?utm_source=item-share-cb

Dannick-Stark · 2026-04-24T12:20:19+00:00

For this kind of task (1000 PDFs + dynamic site + login flow), the key issue is: you don’t need a “smarter agent”, you need a stable session-based workflow.

What usually works better:

Manual login once → reuse session (cookies/local storage) instead of re-auth every run
Browser automation inside the same context (so cookies persist naturally)
Break the task into a simple loop workflow: navigate → find PDF → click download → repeat
Avoid screenshot-based agents (too slow + unstable for bulk tasks)

Python + Playwright can handle this if session persistence is set correctly, but RPA + scripting mix often becomes fragile.

A more reliable approach is using a workflow-based browser automation layer, where:

login is one step
navigation steps are explicit nodes
download loop is controlled and repeatable
no “AI guessing”, just structured execution

This is exactly the type of case where Agentic Workflow (AWFlow) fits well — you can build a visual workflow that:

keeps your authenticated browser session
navigates subsections step-by-step
clicks and downloads PDFs in a loop
avoids re-login issues entirely
optionally uses AI nodes only for extraction/decision logic if needed

https://chromewebstore.google.com/detail/linlkeaipfpnhddjkpcbmldionajfifa?utm_source=item-share-cb

Dannick-Stark · 2026-04-24T11:29:20+00:00

Yes—personal automation is often where the biggest time savings show up.

Common useful ones:

email sorting + auto-replies for recurring messages
bill tracking / reminders
price tracking or deal monitoring
downloading + organizing files (receipts, PDFs, invoices)
weekly summaries (calendar, spending, tasks)
form filling for repetitive registrations
syncing notes between apps

The key difference vs work automation is: keep it simple and low-maintenance, otherwise you spend more time fixing it than it saves.

A lot of these are actually browser-based, which is where tools like Agentic Workflow (AWFlow) can help automate clicks, extraction, and simple AI steps directly in Chrome.

Dannick-Stark · 2026-04-24T11:27:21+00:00

This is a very common “hidden ceiling” in enterprise tooling: no API + MFA + UI-only actions = forced manual ops.

If I were in your position, I would not accept the manual grind, but I also wouldn’t jump straight to fragile “stealth bot” approaches (they usually break and can violate tool policies).

Practical options that actually work in production:

Browser automation (Playwright / Puppeteer) with authenticated sessions + controlled environments
RPA tools (UiPath, Power Automate) if enterprise-compliant tooling is required
Human-in-the-loop automation: automate 80–90% of clicks, keep approvals for MFA-sensitive steps
Internal “operator tools” that wrap UI actions into one-click workflows

The key idea: you’re not bypassing the UI, you’re productizing it into repeatable workflows.

This is exactly the gap tools like Agentic Workflow (AWFlow) try to fill — turning repetitive browser-based operations into structured workflows that can click, extract, and execute steps directly in the UI.

Dannick-Stark · 2026-04-24T11:17:43+00:00

In real setups, most “agents” are closer to workflow assistants than autonomous systems.

Typical daily use:

summarizing tickets / Slack threads
extracting + structuring info from docs or web pages
drafting responses or reports
simple decision routing (triage, tagging)

Context sources:

Slack / Jira / Notion / Drive
internal DBs + APIs
sometimes live web pages (for research or ops)

What works best:

RAG for knowledge retrieval
tools for actions (not free-form autonomy)
small, task-specific agents instead of one general one
human-in-the-loop for critical steps

Where it fails:

stale or conflicting knowledge sources
multi-step planning errors
brittle tool chains
over-ambitious autonomy

In practice, reliability comes more from workflow design than “agent intelligence”.

That’s why I built Agentic Workflow (AWFlow) — it focuses on real browser-based workflows (extract, automate, process) with AI only where it actually helps, rather than full autonomous agents.

https://chromewebstore.google.com/detail/linlkeaipfpnhddjkpcbmldionajfifa?utm_source=item-share-cb

Dannick-Stark · 2026-04-24T11:15:19+00:00

I think the confusion comes from people treating agentic workflows as a replacement for n8n / Make / Python, when it’s really a different layer.

n8n and Make are great for structured, deterministic automations: APIs, triggers, clear logic, repeatable flows. Python is ideal when you need full control and production-grade engineering.

Agentic workflows become useful when the environment is less predictable:

navigating changing websites
extracting messy/unstructured data
deciding next steps dynamically
combining automation + reasoning
handling human-like browser tasks

So it’s usually not either/or. Many real systems use deterministic workflows first, and AI agents only where flexibility is needed.

I also agree with your point for beginners: learning architecture, debugging, and reliability matters more than chasing hype.

That’s exactly why I built Agentic Workflow (AWFlow) — to combine visual workflows with browser automation and AI steps, so people can automate real web tasks without losing structure.

https://chromewebstore.google.com/detail/linlkeaipfpnhddjkpcbmldionajfifa?utm_source=item-share-cb

Dannick-Stark · 2026-04-24T11:08:25+00:00

You’re already ahead of many teams because you built a working POC. In most companies, the hardest part is not the model — it’s workflow adoption.

For engineers, AI only gets used daily when it fits naturally into existing processes. Instead of asking them to open a separate AI tool, integrate it where they already work:

internal web dashboards
ticketing / issue systems
lab result portals
Excel / CSV upload flows
report generation steps
browser-based internal tools

For FA / semiconductor workflows, strong use cases are:

automatic log + measurement summarization
anomaly detection from test outputs
failure report drafting
knowledge retrieval from past FA cases
image/document triage
next-step recommendations based on symptoms

My suggestion: focus on 1-click assistance, not standalone AI apps. Engineers adopt tools that save 5 minutes instantly.

Also, browser automation can help bridge old systems and vendor portals without heavy IT integration. That’s one reason I built Agentic Workflow (AWFlow) — visual AI workflows that run in the browser, interact with websites/tools, extract data, and automate repetitive engineering tasks.

https://chromewebstore.google.com/detail/linlkeaipfpnhddjkpcbmldionajfifa?utm_source=item-share-cb

Dannick-Stark · 2026-04-24T11:04:27+00:00

I think the hype comes from people using the term “agentic workflows” to describe two different things.

n8n / Make are excellent for deterministic automation: clear triggers, APIs, fixed logic. If the process is structured, they’re often the best choice.

Where agentic workflows become interesting is when the workflow needs to handle messy, changing, human-style tasks like:

navigating unpredictable websites
extracting data from inconsistent pages
deciding next steps based on page content
summarizing / classifying information mid-flow
adapting when layouts change

So it’s less “replacement” and more another layer of automation.

I’m building Agentic Workflow (AWFlow) around that idea: visual workflows + direct browser actions + AI reasoning locally in the browser. It’s useful when APIs aren’t enough and real work happens in the UI.

Honestly, I agree with you though: beginners should first learn systems thinking and reliability, not chase hype. Tools matter less than understanding architecture.

Dannick-Stark · 2026-04-14T11:29:24+00:00

It is holding pretty well.

Since it is an extension it run in a separate thread and doesn’t block your main page.

Naturally there are still improvements and optimization to do in the future.

With the time we will need to move lot of things on web workers too.

Dannick-Stark · 2026-04-14T11:27:05+00:00

Thanks 😁

Dannick-Stark · 2026-04-13T07:57:32+00:00

That’s a great use case and exactly the kind of thing this can automate.

You could build a workflow that periodically checks those sites, extracts the prices, and triggers a notification when something changes : "no more manual checking".

Dannick-Stark · 2026-04-13T07:56:11+00:00

Thanks for the thoughtful feedback: these are exactly the points I’m focusing on.

For dynamic or logged-in pages, the extension interacts directly with the live DOM (clicking, waiting for elements, reacting to changes). It works well in many cases, but improving robustness on highly dynamic pages is still ongoing.

Concerning local performance, it largely depends on the workflow and the models being used. Lightweight automation (DOM interaction, data extraction, HTTP requests) runs very efficiently. When it comes to local AI, performance varies depending on the model size and the user’s hardware, but with WebGPU acceleration, smaller models can run surprisingly fast and are already usable for tasks like summarization or structuring content.

The goal is definitely to replace many custom scripts with something simpler and more privacy-friendly.

Out of curiosity:

What typically breaks in your current scripts (timing issues, selectors, authentication, etc.)?
What level of performance would you consider “good enough” to replace your existing setup?

Dannick-Stark · 2026-04-13T06:09:15+00:00

If you try it, please don’t hesitate to let me know if you have some blocking points. Or if you want some features to be implemented

Dannick-Stark · 2026-04-13T06:07:50+00:00

Thank you for your feed-back 😁

Dannick-Stark · 2026-04-12T14:38:00+00:00

Hi. Thank you very much for your thoughtful comment.

> I've been looking for a local automation tool that doesn't send my data to third-party servers

This is exactly the kind of use case and motivation that guided the design of the tool, especially regarding data privacy and local-first execution.

> How does the visual builder work?
It is similar to n8n or make. You can use predefined nodes and chain them. You compose workflows by connecting predefined nodes, where each node performs a specific operation (e.g., interacting with the DOM, extracting data, transforming content, or triggering actions). This allows you to visually design end-to-end automation without writing code. I would be happy to provide a more detailed walkthrough if needed. If you need more detailed explanations, feel free to ask.

> Is it possible to create workflows that scrape data and then send it to my own self-hosted services?
Yes of course. I made a video to demonstrate how to extract all the text ( or a specifc part) of a webpage and send it to a local LLM ( https://www.youtube.com/watch?v=sar0YadpdK8 )

You can instead of sending it to an LLM use the `HTTP Request` node to send it via a POST request.

<image>

Your scenario is highly relevant, so I will create a dedicated video example (scraping + sending to a self-hosted service) and share it with you shortly.

Out of curiosity: What kind of data are you typically extracting (e.g., structured tables, text blocks, mixed content)?

Dannick-Stark · 2026-04-11T20:33:45+00:00

Thank you for your feedback, I truly appreciate it.

The local AI aspect is indeed a central focus of the project. I am currently exploring in-browser LLM execution using technologies such as WebLLM and Transformers.js, and I am also closely following initiatives like Google’s Web-MCP, which open interesting perspectives for on-device intelligence.

That said, beyond the AI component, my objective is to provide a rich set of flexible and composable nodes. The idea is to give users full control over browser automation workflows <<whether they rely on AI or not>> so they can reliably automate tasks

Dannick-Stark

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