Conquering Every Challenge with Quality Engineering
A graphic novel series that offers solutions to complex real-world enterprise challenges
About the Series
Every enterprise has a story worth sharing: a system on the brink; a team that wouldn't back down; and a glitch that finally met its match.
Stories like these are the stuff of legend. Our graphic novel-inspired storytelling series brings real-world enterprise challenges and quality engineering solutions to life. Through bold visuals and real-world stakes, explore the everyday chronicles of quality engineering (QE) teams. Discover how we changed the game with our superpower, quality engineering solutions driven by artificial intelligence (AI).
We bring you five iconic stories that showcase QE journey in solving real-world enterprise challenges. Your adventure through these zones begins here.
Elixir of Life
When flawed code denies kids critical care
A phone rings in a quite office. The call brings bad news: a newly deployed algorithm at a healthcare insurance company has just denied a critical heart surgery request for a seven-year-old child. This is not an isolated case.
Embedded deep within the system, a flaw in the program is quietly rejecting claims involving pre-existing pediatric conditions. No one knows how many families have already been affected by it.
What follows is a race to detect the flaw, buried in thousands of lines of code, where every faulty denial could mean a child being denied critical care.
Can our band of heroes track down the flaw before the algorithm delivers another life-changing decision that it was never designed to make?
Faster Releases, Better Outcomes
Behind every adventure is a real-world QE capability built to solve complex enterprise challenges. This time, AI determines which tests matter most, helping QE teams catch the issues before they impact the people who depend on these systems.
Driving Quality across AI Value Chain
AI can deliver lasting value only when every layer of its foundation, from data and automation frameworks to legacy systems, is strengthened through quality engineering and governed by AI assurance. Infosys integrates these capabilities across the AI value chain, embedding agentic AI within each layer. This helps enterprises scale AI responsibly and accelerate its seamless adoption.
Quality Assurance for Agentic AI: Four Key Pillars
Infosys’ four distinct QA pillars support successful, scalable, and reliable enterprise AI implementations. Each pillar addresses a specific risk category, helping minimize its impact on users.
The four key pillars are:
AI trust (Assure)
The Infosys BR² framework brings together comprehensive, pre-configured validation checks across business assurance, systematic benchmarking, Responsible AI (RAI), and red teaming. These checks enable enterprises to evaluate AI agents and workflows, while maintaining a verifiable audit trail.
AI strategy and engineering (Orchestrate)
This capability embeds AI agents across every phase of the software testing life cycle (STLC) to build enterprise AI platforms, context mesh ecosystems, AI-first QE frameworks, and agentic QE approaches. Together, these capabilities help organizations accelerate software delivery, enhance quality, and scale AI testing.
Data for AI (Insight)
Robust AI depends on data assurance. This capability safeguards data integrity, readiness, fairness, and quality throughout the AI lifecycle. This extends from sourcing and digital transformation to retrieval and continuous monitoring. By implementing shift-left testing at the data layer, Infosys empowers enterprises to build reliable, compliant, and production-ready AI solutions.
Agentic legacy modernization (Modernize)
Modernizing enterprise-scale legacy systems, including mainframes, technologies, cloud, data, applications, and customer experiences, requires data assurance throughout the transformation. Infosys QE delivers AI-first quality assurance aligned with the 7 Rs of migration: rehost, relocate, re-platform, refactor, repurchase, retire, and retain. Agentic testing further validates these complex, business-critical transformations.
By implementing these four pillars across the AI value chain, Infosys can help organizations mitigate risks and scale AI effectively, while maintaining quality, reliability, and business continuity.
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