Azure AI Search Agentic Retrieval
Go beyond vector search — LLM query planning, parallel subqueries, semantic reranking, and answer synthesis.
Agentic retrieval plans and reasons over retrieval instead of doing a single query. Configuring knowledge bases, reasoning effort, and synthesis for quality and cost is the challenge.
We build agentic retrieval on Azure AI Search: a knowledge base (orchestrator) with a retrieve action over indexed and remote knowledge sources, reasoning effort (minimal/low/medium) to decompose queries into parallel subqueries, semantic reranking of each, result merging, and optional answer synthesis. Core is GA in the 2026-04-01 REST API; synthesis and non-web sources are in 2026-05-01-preview. We wire it to agents and Foundry IQ.
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