AI Performance Testing: How to Scale Agentic AI with Kandasamy Selvaraj

Kandasamy has built a distinguished career translating performance insight into measurable business outcomes. He championed shift-left performance practices and automated performance gates in CI/CD pipelines, reducing production defects by 80% and accelerating deployments by 40%.

His multi-tier optimization work, spanning JVM tuning, database optimization, async messaging, and infrastructure right-sizing,delivered 75% latency reductions while supporting millions of concurrent users. Among his standout achievements is a cluster-enabled caching framework that achieved 85% cache-hit rates and drove 50x throughput gains. He has led multiple large-scale performance initiatives, including engineering Super Bowl–scale event infrastructure from the ground up,stress-testing systems to 10x target throughput and validating resilience for over one million concurrent users, all with zero downtime on game day.

He also conducted real-time, in-flight performance engineering for GOGO Wi-Fi across U.S. commercial airlines, running production load tests above 10,000 feet to tune ATG signal performance (forward and reverse ATG links) and ensure scalable wireless delivery in the air. His broader portfolio spans high-impact performance and scalability engineering across insurance, retail, financial services, healthcare, and airline connectivity industries.

A strong advocate for observability, Kandasamy implemented enterprise APM tooling using a monitoring-as-code approach and deployed chaos engineering practices that achieved 99.99% platform availability. He holds an AWS Certified Solutions Architect credential, a Certified Chaos Engineering Practitioner certification, an APM Associate Certification, a Generative AI Certified Professional credential, and a Post Graduate Program in Artificial Intelligence and Machine Learning.