Personalize Search Results with OpenSearch Agentic Memory
Improving search relevance typically requires complex personalization pipelines — recommendation engines, feature stores, ML models. This session shows a simpler path: multiple lightweight agents that collaborate through OpenSearch's agentic memory to understand and enrich queries in real time. Same query, different results for different users.
Improving search relevance typically requires complex personalization pipelines — recommendation engines, feature stores, ML models. This session shows a simpler path: multiple lightweight agents that collaborate through OpenSearch's agentic memory to understand and enrich queries in real time. Same query, different results for different users — no custom pipelines, no fine-tuning, just memory.
This session is sponsored by OpenSearch
Hajer Bouafif is a Sr. OpenSearch Solutions Architect in Data and AI Search at AWS. With a background in Big Data engineering, she guides organizations in building large-scale, intelligent search solutions.