Scaling AI Observability: Handling Complex Agent Data

AI systems require robust observability for reliability and performance. The volume and complexity of multimodal data present significant challenges for traditional monitoring. This session explores how to use timeline correlations to perform root cause analysis on traces, embeddings, metadata, and raw blobs. It also demonstrates how LanceDB’s architecture enables real-time analysis and efficient root cause analysis for complex agent logs. 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: • Why traditional monitoring fails for multimodal AI data • Using timeline correlations to find the source of bad results • How to perform efficient root cause analysis on raw blobs and embeddings Subscribe to our calendar: https://luma.com/AgenticAIObservability