Why Non-Code-Based Testing Must Become More Autonomous 

As coding has become more autonomous, so has code-based testing. AI agents can now write functions, generate code-based tests, and validate logic in the same workflow. But the other half of the testing equation, the system-level validation of non-code-based testing, hasn’t kept up.  

That disconnect is becoming one of the most critical constraints in modern software delivery. 

The two worlds of testing 

Testing today spans two fundamentally different domains. 

• Code-based testing is fast and automated. Unit tests, contract checks, and schema validation run near the source, inside CI/CD pipelines, and catch issues early and cheaply. 

• Non-code-based testing validates application workflows, performance under different loads, usability, and reliability under real conditions, across the browsers, devices, and environments that customers actually use.  

Both matter. One ensures the code is bug free. The other ensures the application works for real-world users. 

The autonomy gap between code-based and non-code-based testing 

As AI has learned to write code effectively, it’s learned to test code too. If it can create a function, it can generate the corresponding unit test to check its own work. You can see how coding and code-based testing has evolved in tandem using the levels of autonomy framework: 

• Level 1: Humans write all code by hand and manually test it or use scripted, non-AI automation. 

• Level 2: AI suggests snippets and auto-generates simple tests. 

• Level 3: Multi-step agents handle full tickets, write, and validate code with built-in tests under human supervision. 

• Level 4: Developers describe outcomes, and AI builds working systems with self-verifying tests. 

• Level 5: Agents turn specs into production-ready software with continuous, self-governing quality checks. 

Code-based testing has moved up the autonomy ladder at the same speed as coding, because it operates in the same language: code. Automation of non-code-based testing has moved up the autonomy ladder too, but not as quickly. 

Why non-code-based testing hasn’t kept up with AI coding 

Despite new AI capabilities across the industry, non-code-based testing lingers lower on the autonomy scale, because it’s harder to automate. There are a few reasons for this:  

• The end user. Non-code-based tests are more likely to depend on the humans who exercise the product. Even tools that automate non-code-based testing have historically used technologies like screen recorders to capture manual tests and turn those navigations into automated tests.  

• The intuition challenge. The paths through a modern application are nearly infinite. Determining what should be tested, when, and how deeply requires intelligence. To date, that’s been human intelligence, but increasingly we’re discovering that intelligence can come from smarter AI. 

• The real world. Load and performance failures only appear under authentic workloads, on actual devices, networks, and browsers.  

• The definition of correct. Unlike a unit test that returns true or false, an end-to-end test can fail ambiguously. This leads us to ask: was the test badly written, is it flaky, or did the application really misbehave?  

The business-critical bottleneck 

The autonomy gap between coding velocity and the lagging speed of non-code-based testing now represents a real bottleneck in software development, and it’s getting worse.   

When code can be written, verified, and merged by AI in hours, but full-system validation still takes days or weeks, the velocity gains of agentic coding are lost. The result is a choice between speed and certainty, neither of which a business wants to sacrifice. Move too quickly, and you risk pushing unvalidated software into production. Move too slowly, and you forfeit the advantages of autonomous coding. Neither path is sustainable. 

The strategic imperative 

Closing this gap is critically important for businesses to reap the full benefits of the AI-accelerated SDLC. Leaders must rethink their quality strategy around three principles: 

1. Make the most out of code-based testing. Treat specifications as first-class assets, embedding them wherever code-level testing can use them automatically. 
2. Reframe human-based QA as value validation. Apply judgment and user empathy where it matters most: ensuring intuitive, trustworthy experiences that deliver on the promise of the application. 
3. Invest in agentic non-code-based test environments, so testing can match the autonomy of development in pace and intelligence. 

Bringing balance to autonomy 

Autonomous coding can’t deliver its full promise until non-code-based testing catches up to the same level of autonomy.  

That’s what SmartBear is focused on. We’re building systems that help teams scale non-code-based testing to fully keep pace with the velocity and scale of the agentic development cycle. 

Real confidence in software comes when AI coding and intelligent testing scale together. The teams that bridge that gap today will be able to deliver applications at AI-speed, that work for their end users in any environment, that they trust.