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Explore how to build trustworthy agentic AI systems using Databricks' platform in this 22-minute conference talk by Barr Moses, CEO and Co-Founder of Monte Carlo. Discover the critical relationship between data quality and agentic AI performance, understanding how poor data quality can exponentially increase risks in autonomous AI systems. Learn comprehensive strategies for implementing data observability to detect and resolve data issues in real-time throughout your AI pipeline. Examine best practices for constructing robust, error-resilient agentic AI models specifically on the Databricks platform, with emphasis on leveraging the platform's scalability features. Gain insights from real-world case studies demonstrating how organizations successfully combine Databricks' infrastructure with Monte Carlo's observability tools to achieve trustworthy AI outcomes. Master the techniques needed to transform the promise of autonomous intelligence into a strategic competitive advantage for your organization while mitigating the inherent risks of agentic AI deployment.
