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- Top 14 Challenges in Agile Testing [2026]
Top 14 Challenges in Agile Testing [2026]
Agile testing breaks down over changing requirements, flaky tests, and short sprints. Here are 14 challenges in Agile testing and how to avoid each one.
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- Top Challenges in Agile Testing
- Not Keeping Up With Changing Requirements
- Not Planning Cross Browser Testing
- Failing to Incorporate Automation
- Excessive Focus on Scrum Velocity
- Lack of a Strategic Agile Testing Plan
- Considering Agile a Process Instead of a Framework
- Micromanaging Agile Testing Teams
- Incoherency in Defining āDoneā
- Aiming for Perfection By Detailing an Agile Testing Plan
- Mishandling of Carry Over Work
- Lacking Skills and Experience With Agile Methods
- Test Flakiness
- Technical Debt
- Compromised Estimation
- AI Agents in Agile Testing
Challenges in Agile testing come from short sprints, requirements that keep moving, and test suites that cannot keep pace.
A single sprint leaves one window for writing tests, running them, and fixing what breaks, so gaps such as skipped cross browser runs, flaky tests, and unpaid technical debt build up sprint after sprint.
This guide covers the top 14 challenges in Agile testing, how AI agents change the work now, and how to avoid each one.
Key Takeaways
- Changing requirements are the most common source of challenges in Agile testing, so an Agile test plan has to be revised inside the sprint instead of fixed at the start.
- Skipping cross browser testing in a sprint leaves defects that only appear on the browsers and versions an Agile team never ran.
- Test flakiness breaks continuous integration in Agile teams because a suite that fails at random stops working as a release signal.
- Measuring a Scrum team only on velocity pushes stories onto the board faster than testing can clear them, which increases carry over work.
- An unclear definition of done lets a story leave the sprint with code checked in but tests, static analysis, and review unfinished.
- Unpaid technical debt compounds across sprints and eventually takes the capacity an Agile team needs for new user stories.
- The Real Time Testing feature on TestMu AI runs manual and automated tests at scale across over 3,000 browser and OS combinations, which covers the browser matrix a sprint would otherwise skip.
- AI agents draft test cases from a user story, re-resolve selectors after a DOM change, and group run failures, but agent output still needs human review inside the same sprint.
Top Challenges in Agile Testing
Agile testing brings speed and flexibility, but it also comes with its own set of challenges. Teams often struggle with changing requirements, limited time for thorough testing, and maintaining test automation in fast-paced sprints.
1. Not Keeping Up With Changing Requirements
Coming up with a good Agile testing plan is vital, no doubt about that. But if you believe that your plan is fool-proof and you wonāt ever need to make modifications, think again. Most teams waste a lot of time trying to come up with an ideal Agile testing plan.
Now, although how much weād like to achieve it, the truth is a perfect Agile testing plan does not exist. The complex environment wonāt permit it. Sometimes, you have to make changes on an ad hoc basis. Or you might have to remove some processes. All in all, you have to be flexible and adapt to changes in the sprint, of course, keeping in mind that it all aligns with the sprint goal and youād be ahead of all the challenges in Agile Testing.
2. Not Planning Cross Browser Testing
Most firms cease testing when their site successfully runs on primary browsers such as Google Chrome and the Mozilla Firefox. But do you really think you can have a wide customer base if your site runs well only on a handful of popular browsers?
After all, no customer wants to be restricted to a bunch of browsers. It takes away the versatile nature of business. You also canāt assume that if a web application or website works fine in one browser, the same would be the case for others. This is why it becomes important to ensure that your browser matrix is covered while performing cross browser testing. You can refer to our article on creating browser compatibility matrix to solve any challenges in agile testing due to not targeting the right browser!
Moreover, if you are using cutting edge technology, itās also important to check whether your site works well in different browser versions. Itās important to note that cross browser testing provides a consistent behavior across various browsers, devices, and platforms. This increases your chances of having a wide customer base. You can even choose to utilize an online Selenium Grid to scale your cross browser testing efforts.
Achieve the highest browser coverage, accurate testing, and easy debugging with the Real Time Testing feature on TestMu AI. It is an AI-powered test execution platform that lets you run manual and automated tests at scale on over 3,000 browser and OS combinations.
Note: Automate your app tests over 10,000+ real devices and OS configurations. Try TestMu AI Now!
3. Failing to Incorporate Automation
Speaking strictly in business terms, time is money. If you fail to accommodate automation in your testing process, the amount of time to run tests is high, this can be a major cause of challenges in Agile Testing as youād be spending a lot running these tests. You also have to fix glitches after the release which further takes up a lot of time.
If the company isnāt performing test automation the overall test coverage might be low . But as firms implement test automation, there is a sharp decline in the amount of time testers need for running different tests. Thus, it leads to accelerated outcomes and lowered business expenses. You can even implement automated browser testing to automate your browser testing efforts.
Moreover, you can always reuse automated tests and utilize them via different approaches. Teams can identify defects at an early stage which makes fixing glitches cost-effective.
Agentic AI lowers the cost of starting automation mid-sprint. KaneAI, the GenAI-native testing agent from TestMu AI, authors tests from natural language and from the artifacts a sprint already produces, such as PRDs, tickets, and session recordings. Smart element detection re-anchors a step when the UI shifts, and finished tests export to Selenium, Playwright, Cypress, or Appium for teams that keep their existing framework.
4. Excessive Focus on Scrum Velocity
Most teams emphasize maximizing their velocity with each sprint. For instance, if a team did 60 Story Points the last time. So, this time, theyāll at least try to do 65. However, what if the team could only do 20 Story Points when the sprint was over?
Did you realize what just happened? Instead of making sure that the flow of work happened on the scrum board seamlessly from left to right, all the team members were concentrating on keeping themselves busy.
This excessive focus can lead to challenges in Agile Testing and impact overall performance. When preparing for a Scrum Master interview, you might encounter scrum master interview questions that delve into how you manage and address these velocity issues. For example, interviewers may ask how you balance velocity with quality and handle unexpected challenges.
Sometimes, committing too much during sprint planning can cause challenges in Agile Testing. With this approach, team members are rarely prepared in case something unexpected occurs.
Under-committing can provide more room for learning and leaves more mind space to improve on present tasks. As a result, the collaboration between testers and developers gets better and they can get more work done in shorter time spans.
This approach also increases flexibility in the sprint backlog. In case, time permits, you can add more tasks later on. When you are under-committing, you also reduce the chances of carrying over the leftover work to the next sprint.
5. Lack of a Strategic Agile Testing Plan
Too much planning can cause challenges in Agile testing but that doesnāt mean you donāt plan at all! The lack of a strategic plan helps teams focus in the direction they are headed to. After all, can we even dream about delivering a project or pushing a release with no plan at all?
Benjamin Franklin has rightly said, āFailing to plan is planning to failā. Having a basic guide to reach a goal or a vision assists team members in overcoming challenging situations. Therefore, after setting a goal, donāt forget to define the Agile metrics necessary to reach your goal.
For instance, you can divide your plan into different phases. Itās a wise move to arrange meetings from time to time to review the progress and clear doubts. Some things to discuss during the meetings include sprint velocity, task estimation, and stretch goals.
The plan should be rigid enough to provide a direction to the team on how to work and instill confidence in team members. At the same time, it has to be flexible enough to incorporate changes and work on the feedback.
6. Considering Agile a Process Instead of a Framework
Even experienced developers or testers who have been in this for a while tend to think of Agile as just any other process . They fail to realize that itās a framework that defines the entire development process. It also helps various teams in matching their requirements with preset guidelines.
Agile is about fine-tuning the process and making the required adjustments by using empirical data from shorter cycles of development and release. The team members should put their heads together in every sprint to improve with the aim of making each sprint more effective.
7. Micromanaging Agile Testing Teams
In a waterfall model, the management is responsible for setting a schedule and the pace for teams involved. This model has been in existence for a long time, thus making the management stick to previous practices and habits.
But in an Agile project, if the management closely observes and tries to control what the employees are doing all the time, failure in a sprint becomes inevitable. Agile testing teams are self-organizing. They are cross-functional and work together to achieve successful sprints.
Teams comprise motivated individuals who can make decisions and are flexible enough to adapt during times of change. Everyone is equally empowered working towards a common goal. But when you micromanage Agile testing teams, the constant interference can negatively affect the ability of employees to accomplish the goal in their own way.
If you take ownership and empowerment away from the team, there is no point in adopting an Agile framework.
Check out this video on integrating testing into Agile workflows, it breaks down key practices that help Agile teams maintain quality without micromanagement.
8. Incoherency in Defining āDoneā
My work here is done! Sounds so relieving, right? But when a person says this, what do they really mean by done? A developer can just check in the code and say that theyāre done. On the other hand, some other developer can say this only when they are done with checking in, running tests and static analysis, etc.
Every person on the team has a different definition when they say ādoneā. But an incorrect interpretation about the same can put both employees and the management in a pickle. It can lead to incompletion of various tasks which can cause a lot of trouble, especially, at the end of a sprint.
Therefore, itās important for everyone to be on the same page. When someone says that they have completed their tasks, they should maintain clarity and reveal the specifics.
9. Aiming for Perfection By Detailing an Agile Testing Plan
As discussed earlier, there is nothing worse than aiming for perfection and detailing out the Agile testing plan too much. Itās important to note that you canāt have all the information readily available at the beginning of the sprint.
The best thing to do is to settle for an Agile testing plan thatās good enough. This way, you wonāt spend all your precious time planning. When you have more information, add to the Agile testing plan and make it better. Did you just end up with a killer Agile testing plan without wasting time planning it out? Well, thatās the beauty of Agile.
10. Mishandling of Carry Over Work
Now, no matter how much you try to be on time when it comes to accomplishing tasks of the sprint goal, you canāt completely avoid some carry over work. There is always going to be something left over when the sprint ends.
Itās tough to estimate the time the leftover tasks will take. Even if you are done with 75% of the task, the remaining 25% can take up a lot of time. To be safe, never underestimate the amount of work that is remaining. In this case, remember, overestimating wonāt harm you.
Even if you end up overestimating the work, you can always add more if time permits later. But if you tend to underestimate, there are chances that there can be a tonne of leftover work when the sprint ends.
11. Lacking Skills and Experience With Agile Methods
Agile and Scrum have been standard practice for years, yet teams still add testers and developers who have never worked inside a sprint. Itās not possible to get your company to a flying start with sudden implementation of a new framework.
While the lack of experience itself is not a big issue, if you fail to address this in the short term, itās going to cost you for the long haul. There is a risk of your employees falling back into the same old comfortable pattern of work.
The more you delay, the harder it gets to make your employees relinquish their comfort zone. So, to analyze the experience of different team members, hold meetings and conduct a thorough gap analysis. After that, when you get a vague idea, start educating them on the basics and work your way up to the more intricate parts.
12. Test Flakiness
Another obstacle in agile testing is test flakiness. The inconsistency in test results not only undermines the reliability but also poses a major hurdle in maintaining continuous integration and delivery pipeline, which is central to agile methodologies. It can take a lot of effort to find and fix these flaky tests. Flaky tests can also cause people to lack of confidence in the stability and quality of the software, delaying releases and impacting the overall efficiency of the agile testing process.
Note: Ditch flakiness and automate tests seamlessly across 10,000+ real devices and OS configurations. Try TestMu AI Now!
13. Technical Debt
Procrastination is one of the biggest challenges in Agile Testing due to its quick-paced nature. This attitude can pile up to a mountain of technical debt thatās harder to pay off than one might think. Itās tough to pay off the technical debt with the workload of an ongoing task. It also affects what you are currently working on in the case when you get too caught up in clearing the debt.
When you pick up something you put off earlier, the entire sprint will suffer. Sometimes, when the new tasks suffer due to extremely high technical debt, the sprint can even fail. This is one of the main reasons why you should avoid technical debts and overcome the associated challenges in Agile testing.
14. Compromised Estimation
The biggest mistake some teams do is that they start to treat estimations as accurate statistics. Itās important to note that the nature of estimations is vague! They canāt be accurate all the time. But in most cases, bad estimations are a result of the agile testing team failing to see the complexity or the depth of the user story or a task.
For instance, the developer can uncover dependencies in the user story in further stages of the sprint. This leads to unexpected delays by the implementation team. Now, in an agile framework, you can be prepared for minor delays. But what if 10-hours estimation turns to 20?
The team sometimes has to deal with such circumstances. But if compromised estimations occur on a frequent basis, the sprint format is likely to take a big hit. Therefore, you should be extra careful while making estimations so as to avoid inaccuracies as much as possible.
Key Takeaway: Challenges in Agile testing cluster around three habits: over-planning the sprint, deferring automation and cross browser coverage, and treating estimates as facts.
How Do AI Agents Change Agile Testing?
AI agents now take the repetitive part of Agile testing: drafting cases from a user story, re-resolving broken selectors, and grouping failures before a tester opens the report.
- Test case drafting: an LLM reads a user story and returns a first set of test cases, so a tester edits a draft instead of writing every case from scratch.
- Self-healing locators: the agent re-resolves a selector after a DOM change, so a renamed CSS class stops failing every test that touched that element.
- Failure triage: an agent groups a run's failures by stack trace and marks the ones that pass on a re-run, which separates real regressions from flaky tests.
- Model Context Protocol: MCP gives an AI assistant a defined way to call the test runner, the CI API, and the bug tracker instead of a human pasting output between tools.
The limits matter as much as the gains. An agent that generates cases from a user story reproduces whatever the story got wrong, and a self-healing locator can hide a real regression by binding to the wrong element.
Agile teams that use agents still review agent output inside the same sprint, and that review time has to sit in the estimate. Treating an agent as free capacity recreates the compromised estimation problem in a new form.
Key Takeaway: AI agents cut drafting and triage work in Agile testing, but agent-written test cases and self-healed locators still need a human review inside the same sprint.
Final Words!
Always keep in mind, the holy grail of a sprint in the agile is flexibility. There are always going to be times when a particular step does not deliver the expected results. But agile is far from the āplan and executeā approach. You have to be flexible and adaptive.
A deviation in the Agile testing plan or the occurrence of an obstacle is not the core issue here. Instead, how you eliminate as many challenges in agile testing as possible and deal with the existing ones determine the success of your sprint.
To sum up, I would like to say that if you stay mindful of the above challenges in agile testing, the chances of success increases substantially. So, the next time you plan a sprint, keep in mind the challenges in agile testing stated above. Try to overcome as many as possible and youāll definitely notice a positive impact.
I hope you liked the article, and youāre ready to tackle these challenges when and where they occur. Share your challenges with us in the comment section down below. Also, donāt forget to share this article with your peers. Any retweet or share is always welcomed.
Author
Jay Singh is the Co-Founder and Chief Customer Officer at TestMu AI (formerly LambdaTest), with over two decades of experience in sales, customer success, and growth strategy. He has been leading customer initiatives at TestMu AI for more than 8 years, helping scale the platform to serve a global user base. Jay is followed by 15,000+ professionals on LinkedIn, reflecting his influence across the software testing and quality assurance industry. Before TestMu AI, he founded BusinessMojos, later acquired by DAMO Consulting, and VisualMojos Technologies. He also held senior roles at LeadSquared and Harman International.
Reviewer
Maneesh Sharma is Chief Operating Officer at TestMu AI (formerly LambdaTest), where he leads the company's revenue functions across go-to-market, sales, RevOps, customer success, and the partner ecosystem, driving its global expansion. He brings over 28 years of experience across product, go-to-market, and business operations. Before TestMu AI he was Managing Director and General Manager for GitHub across India and APAC, Head of Partner Sales and Ecosystem at Adobe, Chief Revenue Officer at WizIQ, and Head of Products and Solutions at SAP India. Maneesh holds an MBA from IIM Bangalore and a B.E. in Electronics and Communications from NIT Surat.
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