Merge pull request #3119 from hpe-dev-incubator/cms/blog/part-8-agentic-ai-and-qdrant-building-semantic-memory-with-mcp-protocol

Update Blog “part-8-agentic-ai-and-qdrant-building-semantic-memory-with-mcp-protocol”

Author: choukden

content/blog/part-8-agentic-ai-and-qdrant-building-semantic-memory-with-mcp-protocol.md

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 As **Agentic AI** systems evolve from reactive language models into structured thinkers, a new challenge emerges: **how do we give these agents memory?** Not just basic logs or static files, but real, **searchable memory** that understands and adapts to context over time.
 
-This is where tools like **Qdrant** and the **Model Context Protocol (MCP)** come in—a modular pairing that brings semantic search and long-term knowledge storage into agent workflows. Together, they enable agents to not only recall relevant information but to reason across past experiences, making **Agentic AI** systems more intelligent, adaptive, and human-like in their decision-making.\
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+This is where tools like **Qdrant** and the **Model Context Protocol (MCP)** come in—a modular pairing that brings semantic search and long-term knowledge storage into agent workflows. Together, they enable agents to not only recall relevant information but to reason across past experiences, making **Agentic AI** systems more intelligent, adaptive, and human-like in their decision-making.    
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 [Inspired by my Medium post](https://dineshr1493.medium.com/all-you-need-to-know-about-the-evolution-of-generative-ai-to-agentic-ai-part-8-agentic-ai-mcp-281567e26838), this article explores how **MCP**, the **Model Context Protocol**—a kind of connective tissue between LLMs and external tools or data sources—**standardizes interactions** between intelligent agents and vector databases like **Qdrant**. By enabling seamless storage and retrieval of embeddings, agents can now “remember” useful information and leverage it in future reasoning.
 
 Let’s walk through the full architecture and code implementation of this cutting-edge combination.