In today’s fast-paced digital landscape, speed and agility are everything. But as development cycles shrink to keep up with ever-evolving market demands, maintaining software quality can become a costly afterthought.
Traditional testing methods, often relegated to the final stages of the software development lifecycle (SDLC), are proving insufficient, leading to last-minute surprises, spiraling costs, and compromised performance.
This is where shift-left testing comes in. By moving testing earlier in the development cycle, teams can catch issues sooner, reduce risk, and release better software—faster.
And with the power of generative AI now in the mix, shift-left testing is poised for a breakthrough. QyrusAI claims to be at the forefront of this transformation, an range of AI-driven testing agents, tightly integrated with Amazon Bedrock, Amazon Web Services’ platform for foundation model access.
“Shift-left is more than a methodology; it’s a mindset shift that redefines how and when we think about quality,” stressed Ameet Deshpande, head of engineering at Qyrus.
“With QyrusAI, we’re embedding intelligence into every phase of the SDLC, ensuring that testing is not just earlier—but smarter and more aligned with real-world complexity,” he said.
‘Smarter agents’
What QyrusAI basically does, is to run a suite of modular testing agents built on advanced large language models (LLMs) and vision-language models (VLMs), hosted through Amazon Bedrock.
Each agent is tailored for a specific phase of the development process, together forming an end-to-end AI-powered testing pipeline.
Take TestGenerator, for example. Using Meta’s Llama 70B model, QyrusAI reads and interprets requirements documents to generate relevant, detailed test cases.
Then, Anthropic’s Claude 3.5 Sonnet reviews those cases for completeness and identifies any overlooked edge cases or failure points. Cohere’s embedding model helps retrieve specific data from large documents, while Pinecone’s vector database enables rapid semantic search to support dynamic test creation.
“TestGenerator doesn’t just automate test creation, it understands context, infers missing details, and prioritizes high-risk areas,” Deshpande explained. “This helps teams lay the foundation for quality right from the requirement analysis phase.”
Design validation is handled by VisionNova, which turns design documents into testable UI scenarios. Claude 3.5 Sonnet analyzes visual inputs and creates test cases that simulate real user interactions.
By incorporating these AI agents early in the lifecycle, teams can build confidence in both the functionality and design of their applications—before a single line of code is written.
Testing before coding
One of the major challenges of early testing is the dependency on backend services that might not yet exist. API Builder solves this by generating virtualized APIs based on specs, letting front-end teams test UIs with dynamic mock data.
Meanwhile, Echo creates synthetic test data to cover a range of scenarios without relying on real user information.
For exploratory and objective-driven testing, agents like Rover and TestPilot use a mix of LLMs and VLMs to simulate different user paths, uncovering bugs that traditional test scripts might miss. When tests do break, Healer steps in—analyzing script failures and automatically recommending fixes.
“All of these agents are powered by Amazon Bedrock, but what truly brings them together is our internal qai package,” said Deshpande. “It’s the glue that makes QyrusAI’s ecosystem scalable and efficient.”
The qai package, built on top of aiobotocore, aioboto3, and boto3, streamlines access to the diverse range of models on Amazon Bedrock. It enables standardized function calling, JSON output formatting, parallel executions, and quick updates across QyrusAI’s entire agent ecosystem.
“Shift-left testing is more than an optimisation, it’s a reinvention of the QA process.”
– Ameet Deshpande
Early adopters of QyrusAI are already seeing real-world results. Teams report an 80% reduction in defect leakage, 20% decrease in UAT effort, and up to 36% faster time to market, the firm claimed. These gains stem from early test generation, continuous quality checks, and reduced manual intervention.
“Incorporating AI into the earliest phases of development doesn’t just improve testing—it transforms it into a proactive process,” Deshpande noted. “We’ve taken what was reactive and fragmented, and turned it into something cohesive, intelligent, and future-ready.”
A typical workflow might begin with a user uploading a requirements document. TestGenerator analyses it using LLMs to generate test cases. VisionNova and UXtract follow up by transforming designs into detailed steps. API Builder then virtualizes endpoints for early UI testing. This all happens in parallel with development, dramatically compressing timelines and reducing rework.
Role of Amazon Bedrock
QyrusAI is inextricably tied to Amazon Bedrock’s flexibility and performance. With easy access to top-tier models like Claude, Llama, Mistral, and Cohere, Bedrock enables QyrusAI to select the best tools for each job—scaling AI applications without the burden of infrastructure management.
“Amazon Bedrock provides the ideal foundation for what we’re building,” noted Deshpande. “It gives us both the power and the freedom to innovate quickly.”
Thanks to Bedrock’s integration with AWS services like ECS, Lambda, S3, and Pinecone, QyrusAI runs seamlessly in the cloud, with enterprise-grade security, scalability, and performance baked in.
“Shift-left testing s more than an optimisation, it’s a reinvention of the QA process,” Deshpande said.
“By embedding AI into every layer of development, QyrusAI empowers teams to spot defects earlier, respond to change faster, and ship better products with confidence,” he noted.
“Our goal isn’t just to make testing more efficient,” Deshpande concluded. “It’s to reimagine what software quality can look like in the AI age.”
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