Agentic search tuning: faster and better

Getting good results from a search engine is hard. Too hard. We all know that the virtuous circle of search algorithms, then search measurement and analysis, then search tuning, and back to search algorithms. New algorithms are available from search vendor; and there are more and more tools for measurement and analysis (OpenSearch UBI, OpenSource Connections Quepid, OpenSearch Search Relevance Workbench). But experimentation is slow and tuning is still manual.

Now, though, by taking advantage of LLM-based agents combined with interleaved A/B testing, we can automate the process, making it faster and more accurate. Building on an agentic infrastructure, we create a collection of agents, each specialized in a particular business problem and incorporating a variety of search strategies. The agents not only create tests and evaluate them, but also orchestrate their deployment.

Stavros Macrakis

OpenSearch @ AWS