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TLDR: Traditional regression testing is time consuming and brittle. While regression testing is necessary, writing and maintaining automated regression tests costs valuable developer time. LLM-based tools are changing the game, by allowing stakeholders to define tests in natural English. When combined with the right tools, you can also automate running these regression tests, maximizing their value.
Software teams that regularly ship updates need regression testing. It’s non-negotiable. To crib from an old piece of programmer wisdom: everyone regression tests; some teams are lucky enough to do so before their code reaches production.
But here’s the catch: regression testing is slow, tedious, and expensive. Today’s regression testing tools are tightly coupled to your UI design, using techniques like fragile CSS selectors to try to navigate an application that the tools fundamentally don’t understand.
What’s more, you need different tools to handle different kinds of applications. You’re probably not going to have much luck trying to use automated regression testing tools built for web apps on your mobile apps.
The result of this difficulty is that many teams still rely on manual regression testing. Other teams skip it entirely and rely on their production users to let them know when they’ve shipped software that broke something.
AI Regression Testing Tools Are Changing the Landscape
Fortunately for software and QA teams, the landscape is shifting. AI-based tests, which leverage agentic testing, are providing new paths forward that offer very promising early results.
Instead of needing to meticulously script regression tests for your applications with fragile app interactions, you can leverage agentic AIs to interact with test versions of your apps using natural English.
These tools are much better than traditional regression testing tools at navigating software applications of all types. Those skills mean that it’s easier than ever to automatically integrate regression testing into your CI/CD workflows.
In this post, we’re going to break down how to think about AI regression testing and how you can start leveraging it to make your software better today.
AI regression testing still involves defining a script, but instead of a strict, fragile definition, you define the steps and desired outcomes in plain language.
What Is AI Regression Testing?
AI regression testing differs from traditional regression testing in a way that should be pretty obvious: it leverages agentic LLM tools as the primary method engine to run the tests. Traditional regression testing involves defining a strict script for your tests.
AI regression testing still involves defining a script, but instead of a strict, fragile definition, you define the steps and desired outcomes in plain language. This is a powerful unlock for most businesses.
It means that you can expand the pool of people who are capable of writing regression tests. Instead of requiring large amounts of developer time, your primary stakeholders are able to define the details of how the system should work in natural language.
How Does AI Regression Testing Work?
To the end user, AI regression testing is pretty straightforward. You use natural language to define the steps that the agent should take, just like if you were defining a test script for a manual QA process.
At the end of each process, you outline what the agent should expect in terms of visuals and output. Then, you dispatch the agent, either through a synchronous process using a command line tool like Claude Code or OpenAI Codex. This approach will work really well for one-off tests, and many developers find it to be a good way to do some lightweight QA before they ship changes. But it’s not the kind of approach that scales well.
That’s why many teams choose to integrate with a cloud-based tool and run the regression tests as part of their continuous integration flow. That’s a much more scalable solution. If all of the tests work as expected, the testing agent passes. If they don’t, then you get a failure message, and it’s time to debug.
The Technical Details
At a more technical level, there are a number of different ways to connect coding agents to tools like browsers. If you’re trying to connect your local agent to a browser, Claude Code and OpenAI Codex have standard flows for connecting their tools directly to the browser.
You can also choose to use cross-agent tools like the Browser Use toolkit to connect your agents to the browser. These use existing browser APIs and tools like the JavaScript developer console to issue commands to the browser on the user’s behalf. These approaches also work great for one-off testing, and are a terrific way for individual developers to start out running LLM-based integration tests. But if you try to scale this work, you’ll quickly find yourself needing to build custom integrations and complicated platforms to support your CI/CD flow.
In reality, this means that the agent can effectively do anything that you as a user can do in the browser. This is straightforward and simple for you to accomplish without a need to think too much about it.
Non-Browser Integrations
This is where things get trickier. If you’re trying to perform AI regression tests on platforms like mobile applications or non-browser desktop applications, you might find yourself on a tougher road to walk.
Integrating local agentic tools with desktop apps or mobile apps (via an app simulator) is often complicated and tricky. How well things will integrate likely depends on the underlying APIs of your application.
If you’re trying to build these integrations yourself, you’ll find it to be substantially more complicated than the straightforward browser-based integration.
When you’re working with native apps and mobile apps, it pays to invest in a professional solution like Autify Aximo.
Every time you change the layout of the app, you’re going to need to update the testing scripts, lest you get failures that aren’t tied to any actual regression in functionality.
Why Choose AI Regression Testing?
AI regression testing exists as kind of a “best of both worlds” solution to regression testing. Traditional manual QA regression testing allows a team to define what an application should do in natural language. That’s great, because it means your developers don’t need to spend a lot of time scripting tests.
Unfortunately, manual QA is too time-intensive to run on every merge or deployment. You can’t afford the kind of time necessary to run a script of hundreds of tests manually every time you want to push new code.
That’s why many teams forego manual QA and instead choose to script their regression tests. That comes with the other downside: your developers need to spend a lot of time scripting and caring for those regression tests.
They’re brittle and tightly coupled to your app’s visual layout. Every time you change the layout of the app, you’re going to need to update the testing scripts, lest you get failures that aren’t tied to any actual regression in functionality.
That’s where AI regression testing is a winner. It has the flexibility and resilience of manual testing. It runs quickly and returns so quickly you can integrate it into CI/CD flows.
How Can I Integrate AI Regression Testing into My Workflow?
The easiest way to begin integrating AI regression testing into your workflow is to define a series of tests in natural language and run them through a local agentic tool like Claude Code or Codex.
You can start out by typing out instructions on how to access the testing platform, then provide a clear test for the agent to run, allowing the agent to determine whether the test succeeds or fails.
That works for one-off testing flows. The truth is that you’re probably going to run into issues quickly if you try to expand this workflow into a key part of your deployment process. You’ll likely want to build out things like automated test runs, and failure states if any of your tests fail: just like all of the rest of your automated testing infrastructure. If you’re looking to integrate these regression tests into your CI/CD flow, then it makes sense to look into integrating a cloud-native platform for AI regression testing, like Aximo.
You still get the same natural language interface for defining your tests while also getting the ease of use of an automated cloud-based tool that you can run on every deployment.
How Should I Choose an AI Regression Testing Tool?
If you’re thinking about adding AI regression tests to your system, here are the criteria you should be evaluating when you’re choosing a tool:
- Does the tool work across web, mobile, and native apps?
- Does the tool output full text and images when something fails?
- How easily can you define tests? Do you need to use specific words, or can you use natural language?
- Does the tool learn your system as you use it, gaining resilience instead of brittleness?
- Can you run multiple test streams at the same time?
- Can you organize and coordinate tests into logical groupings right within your testing app?
As part of your evaluation process, we’d love to show you what Aximo can do. You can demo Aximo for free today.
FAQ
What's the Difference Between Using a Coding Agent and a Purpose-Built AI Testing Platform?
This is a great question. On the surface, all LLM-based technologies might seem the same, especially if you use the same underlying model, it might seem like you can throw any LLM tech at any problem and get the same results. In reality, that’s not true. Tools like Claude Code are much more effective at writing code than the traditional claude.ai website, even when using the same model. Why? Because Claude Code has access to things like tool use and specific prompts that improve the code it outputs.
The same kind of improvement comes from adopting a testing-focused LLM platform. A testing platform will have specifically-tuned prompts, and also leverage skills like persistent memory and visual verification of functionality. These same tools elevate a purpose-built testing platform over a traditional code-generation application in the same way that code-generation tool is better than a web-base chat window.
When Should We Run LLM Regression Tests?
LLM regression tests are useful at any stage of the development process. They’re most useful when you integrate them into your continuous-integration build process.
That ensures that you’ll identify any regressions from software changes before you deploy them to your production environment and they negatively impact your customers.
What Is the Best Model for LLM Regression Tests?
Here’s the truth: nobody is sure yet! The state of the art in LLM model development changes rapidly right now.
Moreover, it’s very difficult to test LLMs for true effectiveness and efficiency in tasks like these. If your team is already committed to a particular tool like Claude, Codex, or Gemini, it’s probably better to leverage the tool you’re familiar with, rather than trying to find the “best.” Purpose-built platforms abstract this decision — they're trained and tuned on testing-specific workflows rather than depending on whichever frontier model you happen to have access to.
Is LLM-Based Regression Testing Expensive?
This is a common question about any LLM-based technology, but the better question to ask is about the return on investment from adopting LLM-based regression tests. LLM-based regression tests are much faster to write and edit than traditional regression tests. Faster regression testing leads to better, faster releases and less developer time spent maintaining tests. Any LLM-based solution will come with some costs, that part is true. But adopting a purpose-built testing platform that’s tuned for the best results will curb cost vulnerability while giving you better results at the same time.
How Can LLM Regression Testing Go Wrong?
LLM-based regression testing is new technology, and LLMs are not deterministic tools. Your most likely point of failure is going to be tests that wind up “flaky.” Flaky tests are highly frustrating during your build process.
What’s more, if you’re using LLM-based tools to write code for your features, a flaky test might cause the AI agent to modify code that it otherwise wouldn’t need to.
As with all AI tools, you want to build strong harnesses and thoroughly evaluate all of the code that they output as part of your development flow. If you adopt a purpose-built LLM testing platform, they’re deliberately built to protect you from flakiness using visual recognition, self-healing routines and memory functions. That’s the kind of harnessing you’d need to spend time and resources to build yourself. Professional testing platforms will have that harnessing right out of the box.
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