You have a chat app in a Microsoft Foundry project and an Azure AI Search vectorized index. You need to connect to the index to meet the following…

Agentic Retrieval Augmented Generation (RAG) employs a large language model (LLM) as an intelligent agent to orchestrate the entire retrieval and generation process. This agent can decompose a user's complex question into multiple sub-queries, which can be executed in parallel against the vectorized index to reduce latency. The agent maintains the state of the multi-turn conversation, using the history to intelligently plan subsequent retrieval actions. It then synthesizes the information gathered from multiple retrieved chunks to formulate a comprehensive answer. This advanced pattern directly meets all the specified requirements for handling complexity, conversational context, and performance.

A. classic Retrieval Augmented Generation (RAG): This approach typically involves a single, stateless retrieval step per query and lacks the sophisticated planning and orchestration needed for multi-turn context and parallel retrievals.

B. chain of thought: This is a prompting technique that guides an LLM's reasoning process during generation; it is not a retrieval architecture for interacting with a data index.

D. iterative retrieval: This describes a process of refining a search over multiple steps but is only one component of a larger agentic framework and does not encompass the full scope of planning, parallel execution, and conversational memory.

1. Microsoft Learn, "Advanced Retrieval Augmented Generation (RAG) patterns": Under the section "Orchestration and agent-based RAG," the documentation states, "An agent-based RAG pattern involves using an LLM to orchestrate the retrieval and generation process... The agent can break down complex questions into smaller, manageable parts, and use different tools to find the relevant information for each part... The agent can decide to run multiple queries in parallel to reduce the overall latency." This directly supports the use of agentic RAG for complex questions, parallel retrieval, and planning.

2. Microsoft Learn, "Build a chat app with Azure AI Search": In the section on "Orchestration," it discusses how an orchestrator (or agent) manages the flow of a conversation. It explains, "The orchestrator receives a prompt and determines which tool to use... It maintains the context of the conversation." This highlights the agent's role in managing multi-turn conversations to influence retrieval.