Enterprise QA has always run on two major assumptions: tests can be trusted, and tested software is what ultimately gets shipped.
For the past few years, AI has stress-tested those assumptions by generating software faster than many organizations can validate with their existing QA infrastructure. Faced with becoming a bottleneck against releases, many testing teams are experimenting with AI in their workflows as well.
The speed AI brings to testing is plainly visible, while the importance of verification remains top of mind for test and QA professionals. The central challenge with using AI in testing is that the gap between AI speed and verified validation is harder to see. AI is probabilistic by definition, which means that tests generated by AI will inevitably be inconsistent and lead to unvalidated outcomes. This often becomes crystal clear once it’s too late, and a release has failed or authorities are conducting an audit.
Leapwork’s Continuous Validation Platform closes the gap between speed and confidence by ensuring that QA and software teams can utilize AI speed while guaranteeing precision and building trust. We make it possible because our platform is Deterministic by Design.
The concept of being Deterministic by Design is based on three key observations about AI’s place in continuous validation:
- AI belongs in the design phase, where its speed and adaptability are undeniable assets.
- Deterministic execution belongs in the run phase, where repeatability and auditability are non-negotiable.
- Keeping those two systems in their proper places allows enterprises to genuinely stand behind AI-generated tests.
When separated, AI and deterministic execution work in tandem to enable software and QA teams to leverage the speed of AI for testing without sacrificing verifiability or accruing risk.
When a Passing Test Isn’t a True Pass
Understanding why this separation matters starts with considering how a test should behave during a run.
A proper test is deterministic: given the same input and the same application state, the test should produce the exact same outputs every time. Deterministic outputs are repeatable, consistent, and provable, and a test’s predictability is what allows a QA team to confidently declare that a release is ready. Remove that guarantee, and the team is shipping based on hope, not evidence.
Large language models (LLMs), and the agentic systems built on top of them, are probabilistic by design. The same prompt can produce different outputs across runs: some will be right, while others will be wrong.
When an AI model is wrong, it doesn’t fail loudly. Instead, it returns well-formed yet incorrect outputs, all with the self-assuredness of a first grader spelling “February” without the first ‘r’.
In a testing context, this creates a precarious situation: a test that passes but validates nothing. A silent false pass is harder to catch than an outright error, because it gives teams assurance they haven’t actually earned.
This risk is the foundational tension AI introduces into quality engineering.
Agentic AI, which generates, executes, adapts, and makes decisions mid-workflow, adds another layer of complexity. Agentic throughput is significantly higher than generative AI alone. With that scale comes a larger surface area where unpredictable behavior can produce outcomes that are tough to trace, explain, or endorse in a compliance review.
Regulated industries experience this challenge the strongest. In pharmaceuticals, financial services, and manufacturing, a QA process that cannot be fully explained and audited at best doesn’t assure quality. At worst, it’s a legal liability.
This leaves technology leaders with a difficult question: As AI accelerates software testing, can organizations convert probabilistic AI output into deterministic outcomes they can defend?
The answer is yes, if the AI layer is kept in its proper place.
Two Loops, One Boundary
AI and deterministic logic only conflict when they occupy the same layer. These systems are like cats and dogs: keep them in separate areas, and the tension between them cleanly resolves.
This idea is one of the foundational design principles behind both Leapwork Play and Leapwork Flow. Divide the testing process into two distinct loops, and separate them by a strict, validated boundary.
AI contributes most naturally in the authoring loop: interpreting requirements, drafting test steps, generating coverage from recorded application behavior, and turning plain language into working tests. Probabilistic reasoning is an asset here, since creativity and adaptability are precisely what the design phase stipulates.
The execution loop works differently. Once a test crosses the boundary into execution, the AI has already left the building. What runs is a fixed, explicit, inspectable sequence of steps; the same logic, in the same order, every time. No model improvises during a run or reinterprets a step.
The boundary between these loops is where governance happens. Nothing moves from authoring into execution without first becoming validated, repeatable logic. AI operates within a defined set of proven, permissioned actions and cannot generate an operation the platform hasn’t already verified.
The Deterministic by Design Platform
This two-loop, one-boundary structure is the foundation of a Deterministic by Design validation architecture. It operates throughout the entire Leapwork Continuous Validation Platform.
Within Leapwork Flow and Leapwork Play, every release runs under one governed, deterministic layer. Audit trails, explainable logic, and enterprise access controls are built into how the platform works rather than added afterward. GxP, SOX, and data sovereignty requirements are addressed as a function of the architecture itself. Teams move at AI speed and still produce the exact evidence regulated environments require.
Accountability isn’t limited only to the test and its results. When Leapwork Play or Leapwork Flow generate a test, the system creates an evidence link at every step in every test case, tracing each generated action back to its source (whether that’s a requirement, a recorded behavior, an input document). Reviewers can verify that each step reflects actual application functionality rather than something the model inferred or invented.
That evidence layer also serves a practical cost purpose: because tests execute as fixed, deterministic logic rather than live AI calls, there is no model cost at execution time. Enterprises get the speed of AI at authoring and the reliability of deterministic code at runtime, without paying for both indefinitely.
A great illustration of how this paradigm holds up under pressure is self-healing.
When an application changes at runtime, the platform first attempts a pre-recorded deterministic recovery. If AI needs to step in, it operates under strict rules:
- Match the exact element named in the step.
- Never substitute an approximation.
- Cross-reference the screen, the DOM, the test case, and the underlying requirements before attempting any repair.
- Stop to flag the issue rather than proceed with uncertainty.
Each rule exists to protect the integrity of the test record, and a clean stop is always the better outcome over a wrong action recorded as a pass. When a repair succeeds, it’s saved as an explicit, reviewable change; a visible decision, not a silent one remade on every subsequent run.
Closing the Gap Between AI Speed and Deterministic Predictability
The gap between AI speed and verified validation is where release risk accumulates quietly, across test cases that look complete, steps that appear to pass, and outputs that carry more confidence than they’ve earned. Closing that gap requires an architectural commitment to governed, traceable, repeatable execution at every stage of the QA process.
A Deterministic by Design approach is what that commitment looks like. The Leapwork Continuous Validation Platform incorporates governance as a property of the platform’s architecture rather than as a process layered on top of it. The result allows QA and software teams to adopt the best of AI authoring and deterministic execution seamlessly into their workflows.
If your organization is ready to see how that holds up against the complexity of a real enterprise estate, we’d be happy to walk you through it. Schedule a demo, bring your most complex use case, and see what a Deterministic by Design approach looks like across your environment.
