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Blog - Introducing Applause’s First Agentic Workflow

Introducing Applause’s First Agentic Workflow

​It’s no secret that AI is changing the way organizations build and test apps: particularly agentic AI. As a testing partner for many of the world’s technology innovators, we’ve got a front row seat as those developments unfold. But we’re not just sitting back and watching from the sidelines – we’re incorporating agentic workflows into our own roadmap.

In a May 2024 Forbes blog on functional testing, I noted that as digital products get more complicated with different dynamic elements, testing becomes more complex as well. Many organizations, reasonably, turn to automation to improve coverage while controlling costs. But automation, like other types of testing, gets harder and more intricate. Automation alone cannot guarantee great digital quality. It can, however, definitely help.

With that in mind, Applause’s engineering team began experimenting with using agentic workflows to help quickly develop test automation solutions. We set out to accelerate customer onboarding using AI systems to reduce the human involvement in project setup, Github configuration, and even some initial starter tests using the open source Applause automation framework.

This system automates the process of creating and executing web-based test scripts using components and agents, as well as creating the project structure and GitHub repository for the customer. For customers who have opted in to participate, we’ve already incorporated Gen AI into our test case management system to rewrite and update test cases; now we’re talking about a much higher level than just asking AI for a function that does a specific thing. This agentic workflow includes different components and calls for AI to carry out some thinking and processing in addition to generating test cases and scripts. It relies on multiple models, including both small language models and large language models.

The basic workflow:

  • Capturing user inputs and web page data (component)
  • Generating a structured test plan (agentic workflow)
  • Translating the test plan into executable Java code (agentic workflow)
  • Executing the tests and analyzing results (component).

Let’s walk through each of these steps in more detail.

Step 1: Capturing user inputs

User input defines the high-level mission. The user can specify features, test cases, or number of tests to create, or let the AI figure it out – then point it to an app or a page.

Step 2: Generating a structured test plan

The system takes all the information from that page or app, to figure out what the asset is and what types of tests it needs to create. It has to look at the html, pull out the xml, capture screen shots, think about that a bit, come up with recommended test cases, then create a test plan.

Step 3: Writing test scripts

At this point, the system has an understanding of the Applause automation framework, which is open source. It now has to write tests that match those test cases and then produce working, running Java code.

Step 4: Executing tests and analyzing results

As the final step in the workflow, the system executes the automated tests and analyzes the test results to determine which tests passed or failed.

The workflow is currently less autonomous in that it doesn’t iterate or go back and refine itself; that’s planned for a future generation. As we developed our first agentic workflow, we wanted to limit risks. To that end, we focused on:

  • Clearly scoped rules, permissions and access boundaries
  • Audit logs for every agent action
  • Sandboxed execution environments
  • Strict access control and API rate limiting
  • Thorough testing with humans in the loop

The process is a quick start for our new opt-in customers, designed to create an automation project from scratch with initial running tests. We’re breaking up the agentic workflow and exposing each phase individually. This allows users to execute a single piece of the process and review or refine the data, effectively adding more human-in-the-loop touchpoints to deliver high quality tests. From here our automation experts take that project, review the work, and continue to refine and expand to meet the customer's requirements, including coverage needs.

We’re looking forward to evolving this workflow to become more autonomous. We’re examining additional business processes from an agentic workflow perspective to identify places where we can make them smarter, better and more valuable to the business – and our customers.

Apps targeting young people

Naturally, banks and online brokers are also increasingly offering mobile solutions for stock trading. However, this new group of fintech startups has a different structure than traditional providers. As international apps with social media appeal, they are aimed at a particularly young target group of 25- to 35-year-olds who want not only access to stock trading but also a new kind of user experience. It has become clear that accessibility and user-friendliness are key selling points for these new investment apps. For example, according to Bitkom’s Digital Finance Report 2020, 40% of respondents expressed the expectation that “smartphone apps’ ease of use for stock and securities transactions will enable more people to benefit from companies’ performances.”

In a nutshell, the easy access via smartphones makes these “neobrokers” so appealing. Clear design, community integration, and ease of entry has turned UI/UX into an actual product.

Special opportunities – special risks?

Many apps have little to no limit on how small a trade can be, making it possible to buy fractional shares. As mentioned, they charge very low fees — or none at all — and are available outside of regular trading hours. The apps clearly aim to lower the entry threshold for stock trading, and sometimes lure new users with free shares. On the flip side, the apps offer no or minimal investing advice, unlike traditional brokers. Consequently, purchasers must do their own research outside of the app, using articles, forums and social media. This aspect has raised suspicions in the German market. In the survey undertaken for the Bitkom Digital Finance Report referenced above, 69% of respondents stated that “an advisor’s input is absolutely key to making good investment decisions.” As a result, the separation of professional advisory services and the gamification of trading stocks carries certain risks, especially for inexperienced users.

Too much power?

The potential dynamics unleashed by direct market access were demonstrated in an interesting case study in January. Small investors coordinated a purchase of GameStop stock via Reddit to prevent a decline in the company’s value, on which hedge funds had speculated. In fact, the Reddit community’s actions were so successful that U.S. authorities are now investigating the possibility of market manipulation. Outrage erupted, however, when Robinhood simply suspended trading in GameStop shares at the height of the buying frenzy.

Ultimately, the neobroker did have a good reason for halting trading. The security it had deposited with clearinghouse DTCC was insufficient to match increased trading volume. However, this episode illustrates that some luster has fallen from the new market power of small investors: Even trading apps do not eliminate the intermediary function; they only replace it, sometimes with even more opaque conditions than before.

The outlook is promising

And yet, neobrokers are attracting young investors by reinventing the process of investing and stock trading. With pleasing designs and customer experiences geared toward millennials, these apps will be able to gain many users in the next few years. At that point, they will have to show that they can keep up with the momentum that they created. Users expect apps, acting as financial service providers and managers of highly sensitive data, to be error-free at all times and in all places – and rightly so. User trust and compliance with financial rules will play a crucial role in determining whether neobrokers will remain competitive as market penetration continues.

However, the new investment apps’ penetration of the DACH market is still at an early stage. Established providers, especially banking apps, may leverage the trend by incorporating a more attractive UX and simplified investment features into their existing apps. For example, a whitepaper from the Sparkassen Innovation Hub on the topic of changing values recommends “opening up products to small investment amounts” as well as “using a clear, appealing interface (UI), playful elements for data entry and maintenance, [and] the use of status and progress indicators to guide users through processes” to attract a new group of potential investors.

One thing is certain: The phenomenal growth of investment and trading apps, especially in Germany, could be a precursor to interesting developments in the coming years.

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AI Training & TestingAutomated Functional
Rob Mason
Rob Mason
Technology Leader & Expert | Former Chief Technology Officer
Published On: July 16, 2025
Reading Time: 4 min
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