Generate Activity Clusters

Identify Activity Clusters

Activity clusters are distinct operating regions where the system needs to perform differently in order to succeed. Each cluster represents a single scenario — the conditions present in the system, as captured by sensor data — paired with a single "skill," meaning the actions an operator or agent must take to succeed under those conditions. A complex process can contain hundreds of activity clusters, each corresponding to a different combination of conditions and required actions.

Clustering Lens

The clustering algorithm relies on a "lens" to determine how activity clusters are formed. This lens defines the axis along which the state space is divided, and can be applied in a few different ways:

• Cluster by time: For phased processes, clustering by time organizes the state space into regions based on which conditions occur together at the same point in time.

• Cluster by a specific variable: Choosing a variable — such as machines or SKUs — organizes the state space into regions based on similarities between groups within the data.

• Cluster in aggregate: Aggregate clustering considers all variables at once, forming clusters based on the combined similarity across every dimension of the data simultaneously.

Cluster Labels

Each cluster is defined by a specific range of sensor variable values. To make these clusters easier to interpret, an LLM automatically generates a human-readable label for each region — for example, naming a cluster "Stable Flow" or "Overheat" based on the sensor ranges it covers. These labels make it possible to quickly identify which regions correspond to successful operation versus which represent strain, stress, or failure conditions. Agents use these labeled regions to optimize the process: they learn to identify the successful regions and steer operation toward them.

Why Identify Activity Clusters?

Identifying activity clusters serves two key purposes.

First, activity clusters are foundational to designing a team of agents. Each activity cluster will later be configured as its own scenario, with a dedicated agent assigned to it. That agent then practices within its assigned scenario, receiving feedback until it becomes an expert operator for that one specific skill.

Second, activity clusters help build a deeper understanding of the task itself. Domain experts will typically recognize most of the clusters from their own experience, confirming that the analysis reflects real-world operating conditions. At the same time, the clustering process often surfaces additional clusters that weren't previously known to experts — revealing patterns or edge cases in the data that hadn't been identified before.