Building Quality-Driven Agentic AI in Noisy Big Data Environments

Building reliable agentic AI systems in prod environments presents unique challenges when dealing with massive, noisy datasets. This webinar shares hard-won lessons from developing an AI agent that processes millions of K8s events daily to deliver autonomous troubleshooting that reached 95%+ accuracy in benchmarking.

The fundamental challenge isn’t LLM capability; it’s building systems that maintain reliability when 90% of your data is noise. We’ll explore why most agentic AI fails in production: hallucinations masquerading as insights, inability to validate reasoning chains, and the brittle nature of RAG systems when dealing with complex, interconnected failure modes.

Gain practical tips learned through painful prod iterations: how to build validation frameworks that catch LLM errors before they reach users, architectural patterns for constraining problem spaces without losing effectiveness & methods for creating evidence-based reasoning that can be audited & improved systematically.