- 📊 GPT-4V scored highest on the ScreenSuite benchmark. It reached 73% accuracy in GUI interaction tasks.
- 🔍 Vision-only agents can work with interfaces using only screen inputs. This lets them automate things without needing complex setup.
- 🌐 GUI agents are naturally multilingual. They understand UI parts no matter what language is on the labels.
- ⚠️ Today's VLMs often make up facts or misunderstand visual data. This makes them hard to use in important work.
- 🔧 Hybrid methods use both vision and API access. These are proving to be the most practical way to get strong automation.
GUI agents are a big change in automation. They do not rely on APIs or backend connections. Instead, these agents work with software by looking at and understanding what is on a screen. Then they act based on what they see. VLMs are getting better. Because of this, people are testing these agents in more real-world situations using tools like ScreenSuite. This article looks at what GUI agents are, how VLMs power them, and what they need to do to become reliable digital workers.
What Are GUI Agents?
GUI agents are a new kind of smart digital worker. They work with software interfaces like people do. They look at the graphical user interface (GUI). Then they find things like buttons, forms, menus, and checkboxes. And then they do things like clicking, typing, or scrolling. Traditional automation bots use backend APIs. But GUI agents treat the visual part of software as their work area.
There are two main types of GUI agents:
- Task-driven agents. These are set up to reach certain goals and follow specific steps.
- Reactive interface mimickers. These respond quickly to what they see on screen. They change as they go, based on what is shown at that moment.
These agents work off what they "see." This means they do not need special connections for each app. It allows for automation that works with any interface. So, a GUI agent can work with almost any software. The interface just needs to be visual and understandable.
Remote work and digital tool use are increasing a lot. Because of this, GUI agents are becoming an important tool. They range from customer service bots to process automation tools. These agents offer a way to copy how people use many UI environments. It also saves money.
The Rise of Vision-Language Models (VLMs)
Vision-Language Models (VLMs) are AI systems that combine understanding language with seeing things. These models can look at images like screenshots, UI parts, or icons. And they look at text like labels, instructions, or what is on the screen. This strong combination lets agents work much like a person. They are programmed not by rules or backend data, but by understanding what they see.
Some popular VLMs are:
- GPT-4V by OpenAI
- Claude Opus by Anthropic
- Gemini Pro by Google DeepMind
VLMs are trained on huge amounts of text and visual data. They can look at a webpage, understand what it means in that situation, and then describe or act based on what it contains. For example, if you give a VLM a screenshot of a dashboard, it can:
- Find navigation menus, form fields, or call-to-action buttons.
- Understand user instructions like “Click the Reports tab and download the March summary.”
- Do step-by-step actions in a logical order.
This opens many chances in automation, QA testing, digital access, and more. It also makes it easier to start automating software actions. This is especially true when a platform's APIs are limited, old, or not there at all.
What Is ScreenSuite?
ScreenSuite is a new tool for checking performance. It checks how well GUI agents work in real visual settings. It was made to copy how people actually use different UIs. And it has many different tasks. These tasks show the true difficulty and details of digital work.
ScreenSuite does not check agents based on code access or structured backends. All its tasks are visual-only. This means agents only get images (screenshots) and plain language instructions. They must perform based only on what they can see.
Here are some tasks in ScreenSuite:
- Browser moving around: This copies how a user would find content or settings on web pages.
- Editing work: This uses tools like form builders, email editors, or content dashboards.
- UI finding your way: This means understanding how to use new interfaces.
It supports common apps in areas like:
- Productivity (e.g., Google Workspace, Notion)
- Communication (e.g., Slack, Gmail)
- Moving around and utilities (e.g., settings interfaces, CRM panels)
By checking agent performance in these settings, ScreenSuite gives a good way to tell if these agents are ready to be used for real work. It does this without needing special code or deep access to systems.
Why Visual-Only Evaluation Matters
Most real software use happens in GUI settings. For example, setting up marketing campaigns or updating online stores. Most digital work needs people to do visual actions like clicking, typing, or dragging files.
That is why checking GUI agents using only visual methods matters:
- Like real life: ScreenSuite uses only screen captures and text instructions. This copies human behavior better than tests that use APIs.
- Lower setup costs: Teams do not need to build complex backend connections to test or use automation systems anymore.
- Works for many apps: One agent trained on many visual UIs can be used across hundreds of tools with very few changes.
This model helps agents work with new things. And it makes automation much easier to get for non-developers, small business owners, and freelancers. These groups usually do not have much DevOps help.
Strengths of Vision-Only Benchmarks
Vision-only benchmarks like ScreenSuite offer strong benefits. When you check GUI agents, you can see many benefits:
✅ No Integrations Required
Agents that work only with visual data do not need technical connections to software. This means you do not need:
- API tokens
- Access to developer setups
- Maintenance when software updates
This means you can automate Salesforce, Google Docs, Canva, or other interfaces with no extra engineering work.
🌍 Works for Many Platforms
Agents trained through visual multitask learning often learn to work with many different apps. For example, an agent might have learned to find “Submit” buttons in 100 apps. Then it is much more likely to find a similar action in a new app. This is true even if the button looks or is colored differently.
🧑💻 Easy for Non-Programmers
Vision-driven agents offer real no-code ways to work. A user might just give a screenshot or say what they want. And the agent can take it from there.
🌐 Multilingual Compatibility
VLMs read text from the screen. And they understand what it means in context. So, GUI agents become naturally multilingual. This means they can work just as well if the interface is in English, Mandarin, or Dutch.
These strengths offer great flexibility and ability to grow in automating everyday business tasks.
Limitations and Hurdles
GUI agents and vision-only checks look promising, but they have weak points. Some problems still stop these systems from being fully ready for real work:
⚠️ No Structured Data Access
APIs show clear, organized data. But visual interfaces are unorganized and not the same every time. A screen does not make clear:
- Which items you can click
- Hidden things behind tabs or dropdowns
- Errors that you might not see easily
🤖 Things Look Unclear
Many UIs have things that look similar. Think of multiple blue buttons. Or they use unclear labels. This makes it hard to know what action they will start.
🧩 Layout is Sensitive
Small changes on the screen can stop the agent from working right. For example, a different theme color, different screen size, or text in another language. This makes making them strong a big problem.
🧠 Confused by Instructions and Making Things Up
VLMs often guess what to do based on not enough visual information. This causes them to make up actions, for example:
- Clicking on buttons you cannot see
- Skipping required fields
- Sending in wrong forms
Each of these problems stops GUI agents from being used safely in places where errors can cost a lot.
ScreenSuite Benchmark Results: How Good Are VLMs?
Hazelwood et al. (2024) did one of the biggest studies comparing GUI agents. They showed how well the top VLMs did on ScreenSuite tasks:
| VLM Agent | Benchmark Score |
|---|---|
| GPT-4V | 73.0% |
| Claude Opus | 67.3% |
| Gemini Pro | 54.2% |
A score of 73% may seem high. But it shows big problems. This is especially true when it is crucial for business work to be reliable. The lower scores on complex, multi-step UI actions show the current limit of what these agents can do without problems.
Even top agents like GPT-4V make errors on tasks that include:
- Scrolling through long forms
- Working with pop-up windows inside other pop-up windows
- Doing 'if-then' steps (e.g., “If this, then do that”)
The benchmark shows how much progress has been made. But it also shows how much more work is needed.
Implications for Bot-Engine and Visual Automation
Platforms like Bot-Engine are in a good spot to use this new field. With GUI agents and VLM connections, Bot-Engine can offer:
- Scale faster: Users can put bots to work across platforms like Make.com, GoHighLevel, or Gmail without needing to code.
- Works everywhere: One automation system that uses vision can handle many CRMs, website builders, or emails.
- Quick setup: Setting up a bot becomes as fast as uploading screenshots and writing down task goals.
These features show a future. In this future, automation platforms do not just promise no-code. They deliver it. They use AI that sees, reads, and acts just like a human assistant.
GUI Agents and Multilingual Workflows
Perhaps one of the most important things about GUI agents is their ability to work in many languages.
A GUI agent that uses a VLM does not have to be changed or set up for each language. Instead, it:
- Reads buttons and labels in almost any language.
- Understands instructions no matter what the local settings are.
- Finds UI patterns, no matter the language.
This makes GUI agents global automation tools. They grow easily across many different markets. For businesses growing into other countries, this can be very helpful. You can set it up once, change it for local needs easily, and grow without building new systems.
Are GUI-Only Agents Ready for Real Work?
Today’s GUI agents are strong, but not perfect. They still find it hard to do tasks like:
- Long-term memory (knowing what they clicked five steps ago)
- Fixing errors right away
- Places where security is important (like financial dashboards)
Most experts agree. A full vision-only agent is not ready for big companies yet. But a mix of methods is proving to be the most practical way. In this mix, vision helps with simple actions. And APIs handle crucial or private steps.
What Better Benchmarks Could Look Like
To make GUI agents more ready, we need better and more detailed tests. Future tests should include:
- Memory tests: Check if agents can recall a series of actions. And if they can change what they do based on past actions.
- Copying tools: Use custom CRMs, spreadsheets, and CMS copies. This helps test detailed use.
- Watch click paths: Track each action. Then compare it to how a person would ideally click.
By making benchmarks better and show more real-life situations, developers can train smarter agents that can be trusted more.
Vision-Only vs. API-Based Automation
Each way has good and bad points. It is important to know when to use each one:
| Method | Strengths | Weaknesses |
|---|---|---|
| Vision-only | Works everywhere, no setup, works with many languages | Can get visual errors, slower |
| API-based | Fast and exact, easy to fix problems | Needs developer access, hard to move to other places |
In real use, combining both methods often works best. Use vision for most common tasks. Use APIs for important logical steps.
What’s Next for GUI Agents in Automation?
GUI agents are at the forefront of new automation. Models like GPT-4V are getting better. And benchmarks like ScreenSuite get better too. So, we are moving toward a system where bots will:
- Understand any app just by seeing it.
- Work like smart assistants.
- Work across different languages and countries.
- Need no setup to start.
When this happens, GUI agents will become the digital workers that small teams and large companies will use for common, repeated tasks.
How Bot-Engine Can Lead the Way
Bot-Engine has a good chance to lead the next big change in visual automation by:
- Adding screenshot-based GUI agents to its no-code tools.
- Letting users upload UIs to train agents or run them.
- Using ScreenSuite results to keep training and making agents better.
- Helping with work that crosses languages for clients all over the world.
By using VLM-powered GUI agents, Bot-Engine can become the main platform for digital bots that work with any language and are easy to use.
Are Vision-Only Benchmarks Enough?
Vision-only benchmarks are an important start, but not the end goal. They offer:
- A fair way to measure how well GUI agents can be used.
- A way to copy how real users act.
- A place to test how to make agents work better with new things.
However, models need long-term memory, understanding of what came before, and handling problems. Until then, vision-only agents should be used carefully. The future will most likely be a mix. It will combine vision, APIs, and logic in a smooth way.
For teams serious about new automation, that mixed balance is not just great—it must happen.
Citations
Hazelwood, K., et al. (2024). ScreenSuite: An Evaluation Suite for GUI-Based Vision-Language Agents.
