NextGen Search - Where AI Meets OpenSearch Through MCP

"As keyword search reaches its limits, the industry is shifting toward semantic, multi-modal, conversational, and agentic AI search that understands user's intent, context and empowers users to get insights through natural-language queries without needing technical skills or custom application development. Next-generation context-aware conversational search solutions can be built using OpenSearch and AI agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP). MCP bridges AI agents and OpenSearch for creating intelligent search applications."

"AI agents (specialized AI applications) are LLMs equipped with role, task, and context management capabilities. A typical AI agent integrates an LLM for reasoning, Memory for maintaining relevant context across interactions, Tools for extended capabilities, and Retrieval Augment Generation (RAG) for selective knowledge retrieval."

"The proposed architecture brings these components together through three layers: an agentic layer for intelligence, an MCP protocol layer (MCP client & server) for communication, and a data layer for indexing, search, and analytics. MCP server deployment patterns include local, remote, managed hybrid (on-premises/cloud), and cloud-native deployments, with each option offering different trade-offs based on organizational needs."

Keyword-based search is reaching its limits, prompting a shift to semantic, multi-modal, conversational, and agentic AI search that interprets intent and context. Large Language Models power AI agents that combine role, task, and context management with Memory, Tools, and Retrieval-Augmented Generation to reason and fetch selective knowledge. Model Context Protocol (MCP) bridges AI agents and OpenSearch, enabling context-aware retrieval and interaction. A three-layer architecture organizes components into an agentic intelligence layer, an MCP protocol layer (client & server), and a data layer for indexing, search, and analytics. MCP server deployments span local, remote, hybrid, and cloud-native options with differing trade-offs.

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