Artificial Intelligence is increasingly being integrated into modern applications. From generating content to analyzing data, AI systems are capable of performing complex tasks in the background.
However, many applications face a common problem when integrating AI features.
Users often cannot see what the AI system is doing while it works
This lack of visibility can make even a well-functioning system feel confusing or unreliable.
To address this, a concept called AG UI (Agent UI) is gaining importance.
What is AG UI?
AG UI (Agent UI) is an interaction layer designed to connect AI processes with the user interface in a transparent way.
Instead of hiding the AI workflow behind a loading spinner or blank screen, AG UI allows the system to show users what the AI is doing in real time.
For example, an AI task might involve several steps:
- Fetching data
- Processing the information
- Running analysis
- Generating the final output
AG UI makes these steps visible to the user through the interface.
This helps users understand that the system is actively working and progressing toward a result
Problems Without AG UI
In many AI-powered applications today, the interaction flow looks something like this:
- A user clicks a button to start an AI task.
- The screen shows a loading indicator.
- The user waits without any visibility into the process.
During this time, users often wonder:
- Is the AI still working?
- Did something fail?
- Should I refresh the page?
Even if the AI is functioning correctly, the experience can feel broken because the user cannot see the progress.
This leads to several issues:
- Reduced user trust
- Multiple repeated requests
- Poor user experience
The problem is not always performance.
Often, it is simply a lack of transparency.
Challenges Teams Face While Solving This
Many development teams try to solve this problem by adding small UI improvements such as:
- Loading spinners
- Static progress bars
- “Processing…” messages
While these elements indicate that something is happening, they do not reflect the actual progress of the AI task.
Another challenge is that teams often build custom progress tracking systems for each project. This can lead to:
- Repeated development effort
- Inconsistent user experiences
- Additional maintenance overhead
There is also no standardized way to communicate AI progress from the backend to the frontend.
This is where the concept of AG UI becomes useful.
Our Approach
To address these challenges, we explored how AI systems naturally execute tasks.
Most AI workflows consist of multiple internal steps such as retrieving data, processing it, and generating responses.
Instead of hiding these steps, we asked a simple question:
What if the application could show these steps to the user while the AI is running?
By exposing these stages as progress updates, the AI interaction becomes clearer and easier to understand.
This approach allows users to follow the process rather than waiting in uncertainty.
Our Solution: elastikteams_ag_ui
Based on this approach, we developed a lightweight package called elastikteams_ag_ui.
The goal of this package is to help developers easily implement the AG UI concept in their applications.
Just like the AG UI concept itself, our package is also framework-agnostic.
This means it is not tied to any specific technology stack and can be integrated into:
- Frontend frameworks like React, Angular, Vue, or plain JavaScript
- Backend systems like Node.js, Python, or any AI service
The goal of this package is to help developers implement AG UI without worrying about framework limitations.
Instead of building custom progress-tracking logic for every project, developers can use this package to send structured updates from the backend to the frontend.
These updates can include information such as:
- Current AI execution step
- Progress updates
- Generated content
- Completion or failure status
This enables applications to display real-time AI activity in a consistent and transparent way.
How It Works
The idea behind the package is straightforward.
When an AI process starts, the backend sends small progress updates during different stages of execution.
For example:
Step 1 – Fetching required data
Step 2 – Processing information
Step 3 – Running AI analysis
Step 4 – Generating response
Each of these steps can be communicated to the frontend.
The user interface can then display this information in a structured way, allowing users to see the progress of the AI task as it happens.
From the user's perspective, the experience becomes much clearer:
Instead of waiting without context, users can follow the workflow as it progresses.
Why This Matters
AI systems are powerful, but they often feel like a black box to users.
AG UI helps solve this problem by making AI workflows visible and understandable.
By exposing AI progress in real time:
- Users gain confidence in the system
- Applications feel more responsive
- Developers can create better user experiences
The AI itself may not change, but the way users interact with it becomes significantly better.
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