Tech leader and innovator Ankush Sharma drives a reliability revolution in AI

This inventive researcher and technology leader is well on his path to redefine trust in the world of artificial intelligence.

One thing that has evolved at breakneck speed in recent years is, hands down, artificial intelligence (AI). However, amidst all these developments across sectors worldwide, driven by AI, the question arises how truly people can rely on it or, more so, trust it. As Large Language Models (LLMs) step into crucial roles across finance, healthcare and climate technology, the dangers of outages, system failures and hallucinations grow sharper. This has therefore demanded a rethinking of AI’s very foundation, says tech leader and innovator Ankush Sharma.

Ankush Sharma, a highly driven, inventive researcher and technology leader, is leading this shift. He is the one who patented the framework for LLM observability and reliability, which today is reshaping how enterprises deploy AI. This has allowed him to create a new industry standard that ensures AI systems are not only effective but also trustworthy and reliable. His groundbreaking patent on LLM observability and reliability has been the most talked-about. By applying Site Reliability Engineering (SRE) principles to AI, he has designed a system that monitors, detects, and mitigates failures in real-time. This innovation has paved the way for identifying hallucinations and operational risks before they disrupt users, thereby establishing a new layer of transparency and accountability.

Industry experts have described Sharma’s framework as a “critical missing layer” in the adoption of AI. His patent has helped bring together reliability engineering and AI on a common platform, setting a benchmark for how dependable AI systems should be built. His frameworks have been cited in research and are actively used by enterprise engineers to build observability pipelines for AI. Publications in renowned journals such as ‘Global Scientific Herald Journal, Vision: International Journal of Professional Studies,’ and ‘Adhyayan: A Journal of Management Sciences,’ have validated his methodologies and highlighted their practical impact.

His influence extends across professional media and global platforms. His insights have featured on Global Banking & Finance Review, TechBullion, NewsNation TV and News24, reaching executives and policymakers worldwide. His book, ‘Observability for Large Language Models: SRE and Chaos Engineering for AI at Scale,’ is the first comprehensive guide on operationalising reliability for AI. His talk at Conf42, “Observability for Large Language Models”, translates complex ideas into actionable strategies, earning recognition from global engineers. Additionally, his acceptance into the prestigious Climatebase Fellowship underscores the societal relevance of his work, ensuring that AI remains dependable in climate modelling and sustainability solutions.

Ankush Sharma pioneers reliability revolution in AI, and asserts, “AI’s future will not be defined by how intelligent models become but by how reliably they perform when the stakes are highest.