The Agent Architect #37: Unsupervised Learning for Agents

Pattern of the week

Unsupervised Learning for Agents

What:

Learns patterns from unlabeled data via embeddings, clustering, or anomaly detection, then validates findings with domain experts.

When to use it:

You have raw data without labels and need to discover structure, segment users, or flag unusual cases before applying supervised methods.

Watch out:

Clusters and anomaly scores are unstable artifacts; validation often reveals they don't match real business categories or decision boundaries.

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