Time-Series Semantic Intelligence Agent

A system that treats a live multivariate sensor stream as its environment. It runs anomaly detection and semantic search over streaming time series, then reasons about what it finds — deciding when to retrieve more context, call an analysis tool, or trace a failure back to its cause, while keeping memory of what it has already concluded.

Overview

Problem — most agent frameworks assume a static context window, not a moving stream of industrial observations where anomalies unfold over time.
Approach — a real-time streaming pipeline feeds a time-series foundation model for multivariate anomaly detection and semantic search, with a ReAct-style agent (exposed over MCP) that automates root-cause investigation and failure-mode analysis.
Grounding — a knowledge-graph context layer grounds the agent's reasoning over fleets of industrial assets, so conclusions are tied to the real structure of the system being monitored.