In manufacturing, time-series data is everywhere, but most plants are still relying on static dashboards, lagging insights, and manual root-cause analysis.The result?- Downtime that’s explained, not prevented- Insights that arrive, after the line slows down- Human effort wasted on repeat investigationsAI agents transform the way manufacturers harness time-series data. They process live sensor feeds while simultaneously referencing historical records, enabling instant anomaly detection and context-aware decisions.They can correlate vast time-series data with external factors to uncover insights missed by rigid statistical models.They can trigger actions like maintenance tickets or production adjustments directly from analytics, bypassing manual interpretation steps.They connect the dots across thousands of data streams in real time, automatically identifying root causes and recommending actions on the fly.In the latest episode of the AI in Manufacturing podcast, I sat down with Jeff Tao to learn more about the application of AI Agents for Advanced Time Series Data Analytics. Jeff is the CEO and Founder of TDengine, the developers of TDengine an IIoT time-series database, TDgpt time-series AI Agent, and TDtsfm, a Time-Series Foundation Model.