atai-anomaly-discovery-agent

AD Agent — Managed Anomaly Discovery via the Agents API

The AD agent answers one question: does this window still look like normal operation? It is fitted on normal data only — a reference period from a healthy asset — and everything it flags afterwards is, by construction, something it was never shown.

That premise is the whole reason the agent exists. Its two siblings both need examples of what you are looking for: OSM needs a labelled library of every state, RED needs a handful of shots of a named fault. Neither is available when a machine has never failed, which is the normal condition of most industrial assets — and the condition under which a monitoring system is most valuable.

The graph is the first canonical blueprint with a forked topology:

source → interpolate → window → windowInterpolate → samplingRate → limitValues
       → tee ─┬→ encoder (omega:1.5) ─────→ fuse.emb ─┐
              └→ features (ChannelFeatures) → fuse.feat ─┴→ detector (LOF) → sink

Both branches see the same window: the Omega encoder produces a 768-dimensional embedding, ChannelFeatures produces a handful of per-channel statistics, and ConcatColumnsNode fuses them before the detector head. That fork has consequences documented under Verified platform behavior — it is why long inputs abort and why only one feature mode is reachable.

When to Apply

• Flag departures from normal on an asset with no fault history — nothing has broken yet, so no labelled example of the failure can exist
• Monitor an asset whose failure modes are unknown or unenumerable, where a named catalog would be a guess
• Get a continuous score rather than a class — "how far from normal", not "which of these six states"
• Deploy per-asset detectors as repeatable batch jobs with no client-side ML
• Score a detector honestly when the data has no per-window ground truth, which is the usual case for run-to-failure data

Your own data? The pre-packaged "AD Quick Start" bundles pin a detector fitted on one specific bearing's healthy baseline (the bundled sample slice is from that same bearing). A detector is asset-specific by construction — it encodes one machine's notion of normal — so running your own asset through the quick-start bundle is a transfer test, not a deployment. For your own data, contact [email protected]: Archetype AI will fit a detector with you, and you create a bundle from the canonical ad blueprint around it ("Bring your own detector", below).