Without memory, every behavioral scoring session starts from zero. With memory engines, the behavioral AI platform maintains a persistent, structured, provenance-tracked understanding of each actor across sessions.

The Engines

  • Behavioral Operating Memory — Short-term context: the last N interactions in the current session, held in working memory for the current pipeline run.
  • Knowledge Graph Engine — Long-term: persistent facts about the actor stored as a typed graph (entity → relationship → entity). Every fact has a source, timestamp, and confidence.
  • Embedding Layer — Vector representations of behavioral patterns, enabling semantic similarity search across actor histories.
  • Data Provenance Engine — Tracks the origin of every fact in the knowledge graph: which engine generated it, from which input signals, at what time.

Code Walkthrough

// Knowledge graph: add a fact with provenance
function addFact(graph, subject, relation, object, provenance) {
  graph.facts.push({
    id:         uuid(),
    subject,
    relation,
    object,
    confidence: provenance.confidence,
    source: {
      engineId:  provenance.engineId,
      signalIds: provenance.signalIds,
      timestamp: new Date().toISOString(),
    },
  });
  graph.index.set(`${subject}:${relation}`, object);
}

// Embedding retrieval: find similar actors
async function findSimilarActors(actorEmbedding, topK = 5) {
  return vectorDB.query({
    collection: "behavioral_embeddings",
    vector:     actorEmbedding,
    topK,
    filter:     { domain: context.domain },
  });
}

What to Watch For

  • Knowledge graph facts must be erasable on a GDPR right-to-erasure request. Cascade deletions to embeddings and all downstream facts that were derived from the deleted fact.
  • Provenance chains can grow very large. Index by subject and set a maximum depth for provenance traversal.