AI Agents in QA Testing: Is 2025 The Year Everything Changes?

Is 2025 set to be the year of the AI agent?

It certainly seems to be shaping up that way. With the cost and efficiency benefits of GenAI now firmly established, a recent McKinsey study suggests that 92% of businesses are planning to invest more in generative AI over the next three years.

AI agents - in QA testing and across the workplace - are one of the fastest-growing iterations of this. Deloitte projects that 25% of all businesses investing in GenAI will deploy AI agents across 2025, rising to 50% in 2027. That's a staggering adoption rate for a technology that has only become viable in the past couple of years.

Salesforce and Microsoft have both recently launched platforms to help engineers build their own AI agents. Meanwhile, Oracle has launched over 50 AI agents for enterprise use, with other vendors rapidly expanding their product suites accordingly.

To keep you ahead of the game, here's what you need to know about the potential of AI agents in QA testing and what to expect this year.

AI Agents in QA Testing: Where We Are in 2025?

Unlike AI-based automation, which is rules-based and anchored around predefined workflows, AI agents are autonomous AIs that can make decisions, adjust to feedback, and adapt their approach to unexpected changes in conditions.

Think of them as an autonomous digital coworker, rather than a smart (but ultimately process driven) bit of software.

These are no longer 'the next big trend' or 'on-the-horizon technology' - they are used across businesses, right now, to make huge efficiency gains and generate valuable insights via analyzing vast swathes of data. For example, you might have come across AI customer support agents, or used a conversational interface to structure content for a blog or website.

In software testing specifically, AI agents have the power to unlock transformational change for businesses. Think smarter, faster automation for more complex tests, vastly expanded test coverage, better testing for new technologies, and plenty more besides.

Here's how AI agents are set to transform your QA testing processes this year - and beyond.

8 Ways AI Agents are Transforming QA Testing

1. More Tests, Quicker

Kicking things off with a simple, but useful, effect of AI agents: if you can test quickly, you can test more. If you can test more, you gain more insight into your app and have a greater chance of picking up defects. If you catch more defects before shipping your code, your product quality increases.

AI agents allow software teams of all sizes to expand their QA test coverage, with no extra time cost, even for complex processes like end to end testing. In fact, with an AI agent you'll test far more in the fraction of the time it takes for your external QA processes to come full cycle - which leads us to…

2. An Accelerated Shift Away from External QA

A move away from external QA has been on the cards for a while now, as automation - then AI automation - increased the amount of testing it was feasible to do in house. Autonomous AI agents could be the final nail in the QA coffin for many businesses.

Testing via external QA has always been slow and expensive, with a risk of technical debt thanks to lack of direct engineer involvement in testing.

But what if your AI testing agent could build and run those tests in minutes in house, with just a few plain English prompts, and integrate directly into your CI/CD pipelines? Would you have any need of external QA then?

Probably not, which is why we expect it to shrink over the course of the next 5 years.

3. Better Testing for Conversational Interfaces

GenAI is completely transforming how consumers interact with technology. Think of how often people use conversational AI ('conversational user interfaces', or CUI, to give them their official title) across their everyday lives.

Being able to incorporate CUIs into your products and customer-facing business processes sets you up for long-term success. It offers both convenience and accessibility for your customers and major efficiency gains for your business.

Previously, CUI implementation has been hindered by how complex these interfaces are to build and test. Whilst Big Tech has been able to throw resources behind Siri, ChatGPT, and Google Assistant, smaller businesses have lagged behind with low level adoption (basic customer service chatbots) or none at all.

By generating huge datasets of conversational data for training, and analyzing CUI responses for sentiment and intent, AI agents cut the complexity of CUI testing significantly - and make building high-quality CUIs feasible for a wider range of companies.

4. More Targeted Testing and Development Workflows

One of the most powerful things about AI agents in QA testing is their ability for predictive analytics. As well as simply automating your tests, they can analyze patterns in previous test runs to identify where bugs are most likely to occur.

As well as allowing the AI agent to prioritize high-risk areas for more testing, this helps engineering teams organize their time more effectively. Engineers can dedicate more resources to areas susceptible to bugs and improve overall software quality as a result.

5. (Even) More Focus on App Security and Data Privacy

AI agents use vast amounts of data to run and optimize QA testing processes. Whilst some of this may be artificial data, a portion is likely to be production data gathered from real users.

Without the right safeguards in place, this can pose security and compliance risks for software businesses - with potential reputational and legal repercussions in the event of a breach.

Smart businesses will audit and upgrade their data governance processes alongside GenAI adoption, to make sure that integration of new tech doesn't impact their ability to store data securely and maintain users' trust. Expect:

• A push for more security awareness among employees for faster identification of unusual data access or inconsistent outputs
• Evolution of traditional security tools like encryption, tokenization, and DRM to optimize data security in use and transit, such as continuous encryption

6. Bias Testing Becomes Central to App Quality

Your AI agent is only as good as the data you train it on. Poor quality datasets lead to redundant or inaccurate results - which is absolutely not what you need when trying to build and release a quality product.

As a result, software teams are increasingly focused on maintaining high quality datasets and performing regular testing to ensure outputs are inclusive and accurate. Bias testing - making sure that AI agents aren't influenced by human biases in datasets - is a significant part of this, and can include:

• Using fairness metrics and exploratory data analysis to identify potential biases in data sources
• Diversifying training data to reduce likelihood of bias from limited sources
• Applying algorithmic fairness techniques across the training process
• Using bias detection tools and libraries to find and mitigate bias in AI

7. Acceleration of Shift Left/Shift Right Testing

Shift Left methodology shifts testing to run concurrently with development, so that you pick up bugs earlier, when they are easier and cheaper to fix.

Shift Right methodology emphasizes the role of post go-live testing in continuous improvement.

Both have major CI/CD benefits, and both are becoming increasingly popular among software teams wanting to ship fast and streamline costs whilst increasing product quality.

Shift Left and Shift Right methodologies are made significantly easier by AI testing agents for QA, so expect adoption to continue - and perhaps speed up. This is because the speed of AI agent testing makes it more feasible to run testing and development concurrently, whilst predictive analytic capabilities can help teams accurately identify key monitoring and testing areas after product launch.

8. Less Time Needed For Test Maintenance

Fed up of fragile CSS/XPath locators breaking your test scripts? AI testing agents can adapt test cases to changes in UI or requirements clauses automatically.

What this means for software and QA teams: fewer hours piled into routine test maintenance and fixing flaky tests, and more hours dedicated to valuable development work building new features and updates.

Momentic: The AI Testing Agent for Your Team?

"Momentic makes it 3x faster for our team to write and maintain end to end tests."
Alex Cui, CTO, GPTZero

Momentic is an AI testing agent designed to supercharge your testing processes - maximize speed and coverage, minimize time and expense sunk into external QA, and free your engineers to focus on valuable project work.

If, like Alex and his team, you're keen to save over two thirds of the time you spend on key testing processes, why not schedule a conversation with our founder?