Webinars 
Meet InsightFinder’s new AI agent, ARI, who helps you understand what’s breaking in your stack: fast, with real context. Then go beyond detection to mitigate, fix, and even predict issues before they hit customers. Learn exactly how ARI helps you diagnose your most challenging production problems and how your AI models can be continuously improved through reinforcement learning.
In this webinar, we debut powerful new AI-driven capabilities including a new AI agent, ARI, designed to help you understand issues across your stack of infra, apps, LLMs, and AI agents. You’ll see how to move past just “observability” and into continuous improvement workflows: fast feedback loops that help you mitigate, fix, and predict reliability issues over time by improving the AI you rely on.
Expect real-world, end-to-end demos that show exactly how the new AI agent helps you:
- Find the signal in the noise
- Connect symptoms to root cause
- Turn production incidents into learning loops that make your systems (and your AI) continuously better.
Viewers will learn how to:
- Turn noise into actionable signals across their infrastructure, applications, services, and AI agents
- Go beyond detection with workflows for notification, diagnosis, remediation, and prevention
- Apply practical model fine-tuning and improvement techniques using AI-driven observability feedback loops
- Ensure AI-powered products deliver trustworthy, high-quality customer outcomes in production
Whether you’re responsible for uptime and reliability (SRE/DevOps/Platform), model and data quality (AI/ML teams), or production governance at scale (Directors/VPs/CxOs), this session will give you concrete frameworks, battle-tested practices, and a first look at what’s possible when observability becomes an AI-first improvement system.
Speakers:
- Dr. Helen Gu Ph.D., Founder & CEO, InsightFinder AI & Professor, North Carolina State University
- George Miranda, VP Marketing, InsightFinder AI & co-author Observability Engineering (O’Reilly)
Recorded December 17, 2025
