Engineering the Path to Autonomous Quality

AI that acts, adapts, and delivers in ensuring reliable software 

AI has rewritten the rules of software development. Developers can now generate, fix, and ship code in seconds. This is transformative, but as shared by SmartBear CEO Dan Faulkner, “The tools that help us build software are advancing much faster than the tools that ensure we can trust it.” For software quality to keep up with AI coding, today’s tools must evolve to remain effective.

SmartBear is answering this call. We’re realizing a future where AI evolves from assistant to autonomous partner in ensuring the integrity of your software.

In this future, people won’t need to manually manage test cases or dashboards. They’ll set goals, explore, and push innovative ideas forward, while AI executes that vision. And the impact compounds:

• Software releases ship faster, because autonomous AI agents keep pace with development.
• Applications are tested better, with broader coverage and fewer blind spots.
• Teams can achieve more, because agents fill labor gaps that once slowed progress.

We’re achieving true autonomy by building from first principles

To realize this new reality, software quality solutions must become more autonomous. SmartBear’s approach to autonomy is rooted in first principles thinking. This means we are training autonomous AI agents the same way a human would process and absorb information. We construct the agent’s knowledge context deliberately from the ground up, by collecting raw facts, interpreting telemetry, and forming hypotheses.

If the agent collects the wrong facts or misses a critical piece of input (just like a human would), its understanding is incomplete and the outcome suffers. That’s why knowledge construction is central to the way our agents work. It’s disciplined, explicit, and structured.

This approach is what enables our agents to be fully autonomous. They determine when a task is complete, use the context they’ve built to guide decisions, and adapt as your application evolves. They aren’t merely reacting to text prompts; they’re reasoning over a constructed understanding of your system to ensure your application’s integrity.

This isn’t prompt-based automation

Most AI products today wrap a model in a clever prompt and call it automation. That approach breaks the moment the model is updated, your application changes, or when the prompt stops matching reality.

Our approach is different, because our agents decompose problems into parts the way an engineer would. As an example, consider self-healing of automated tests. A structured autonomous workflow breaks self-healing into stages:

1. Collect the right data:

• Last known successful run
• Previous state of the application
• Recent changes
• Current behavior and artifacts (screenshots, logs)

2. Explore the application as it exists now: Identify whether changes look intentional, stable, or symptomatic of a bug.

3. Make a decision: Update the test with new actions or fail it outright and notify the user.

The key insight is the decomposition. Once the workflow is broken apart correctly, AI is inserted only where judgment is required, ensuring reliability and resilience. It’s engineered decision-making instead of a single shot prompt.

How SmartBear is building towards autonomy

SmartBear is working towards supporting every level of autonomy, whether you’re triggering AI-assisted workflows or orchestrating fully autonomous agents.

Our focus is on delivering kernels of AI-native value: exploration, application modeling, test case creation, execution, and intelligent self-healing. These autonomous workflows will operate independently and improve over time as AI models evolve.

The first fully autonomous workflow we’re building is Autonomous QA, where AI agents can independently explore, test, and report on your applications. Autonomous QA is currently in private beta in SmartBear AI Labs. You can join the waitlist here.

And while we’re building these fully autonomous workflows, teams already gain real benefits today from our range of built-in AI capabilities that make your everyday workflows more efficient. This is AI that delivers value now, while preparing you for the future of autonomous software quality.

The impact of autonomous quality

When quality becomes autonomous, speed and certainty stop being at odds. People bring the strategy; AI brings scale, precision, and execution. Together, they create velocity, coverage, and confidence that weren’t possible before.

And that’s how the best software in the world will be built: by teams who trust AI not just to assist them, but to deliver alongside them.