Systems | Development | Analytics | API | Testing

AI made writing code effortless, but verifying it became the hardest job in software. So we rebuilt Sauce Labs around release assurance to close that gap.

Enterprise QA teams are discovering that deploying machine learning models breaks their existing validation pipelines. Legacy testing environments rely on a simple truth: fixed inputs must produce predictable outputs. Because intelligent architectures operate on probabilistic distributions, deterministic testing alone can no longer guarantee reliability. When conducting a code review or architectural risk assessment, treating an active model as a standard black-box API leaves critical flaws unaddressed.

You've diagnosed the problem. Your agentic AI initiatives are stalling — not because the models are wrong, but because the integration layer underneath them wasn't built for this. Batch data, rigid schemas, fragmented governance, no real-time event delivery. Now the question is: what do you actually build, and how do you build it without tearing down the infrastructure you already have?

If you've been around long enough to remember when iPaaS was the answer to everything, you know the pattern. New paradigms arrive. Someone realizes that wiring it into existing infrastructure is harder than the demos suggested. An integration layer gets built. That layer slowly becomes load-bearing. Eventually, the integration layer becomes the bottleneck. We're at that moment again — except this time, the new paradigm is agentic AI, and the bottleneck is forming faster than usual.

You swapped in a better model. You fine-tuned it. You threw more tokens at the problem. And still — your agents hallucinate, break under load, and deliver answers that were accurate about three hours ago. The model isn't the problem. Gartner recently flagged that up to 40% of enterprise agentic AI initiatives are at risk of failure. Executives see that number and immediately audit their LLM provider. Their infrastructure team. Their prompts.

The AI-analytics category has one operating assumption: AI’s job is to do more of what humans currently do. Adoption is measured by delegation surface. Success is when AI takes the whole task. Ask the executives running AI daily what they refuse to delegate, and you get a different story. Actually, you get the same story, from operators who do not know each other, running different companies, across different functions.

This fall, Tosca’s suite of AI capabilities will be available for on-premises deployments — giving teams the tools to meet the pressure of the AI era head-on. Customers will gain access to leading AI features, including Agentic Test Automation, AI-powered test creation, MCP connectivity, and AI Workspace, no matter their deployment type or existing infrastructure. AI’s unprecedented development means testing practices that were standard a year ago could already be outdated.

A Head of Product at a major sportswear retailer has a brilliant idea: let’s build an app that sales staff on the shop floor can have on their tablets, and ask their questions there and then, where they serve customers. They set about building. In order for the app to answer questions, it needs to have the information from the 2026 Spring/Summer Catalogue, a mammoth manual, let’s say 300k tokens.