{"id":600,"date":"2026-03-31T14:47:33","date_gmt":"2026-03-31T14:47:33","guid":{"rendered":"https:\/\/codingwithramin.com\/?p=600"},"modified":"2026-04-01T15:39:03","modified_gmt":"2026-04-01T15:39:03","slug":"building-a-sharepoint-agent-that-remembers-combining-memory-sharepoint-grounding","status":"publish","type":"post","link":"https:\/\/codingwithramin.com\/?p=600","title":{"rendered":"Building a SharePoint Agent That Remembers: Combining Memory + SharePoint Grounding"},"content":{"rendered":"\n

In Part 1<\/a>, we built a Foundry agent with long-term memory that could remember user preferences, roles, and context across sessions without any custom embedding pipelines. If you haven’t read that post yet, I’d recommend starting there, as this one builds directly on top of that code.<\/p>\n\n\n\n

But here’s the thing: memory on its own is useful, but it’s not transformative. Knowing that a user prefers bullet-point summaries or works in the finance department is helpful context, but it doesn’t change what the agent can actually do. The agent still can’t answer questions about your company’s policies, find that document someone shared last week, or pull up the latest project brief.<\/p>\n\n\n\n

That’s where SharePoint grounding comes in. In my earlier series on Building Intelligent SharePoint Agents<\/a>, I walked through how the SharePoint tool in Foundry Agent Service lets you ground agent responses in your actual enterprise content \u2014 with automatic indexing, semantic search, and permission-aware retrieval built in.<\/p>\n\n\n\n

In this post, we’re combining both capabilities into a single agent. The result is an enterprise assistant that can search your SharePoint content intelligently AND remember what each user has asked about before, their role, their department, and their preferences. It’s the difference between a search tool and an assistant that actually knows you.<\/p>\n\n\n\n

Why Combine Memory and SharePoint Grounding?<\/h2>\n\n\n\n

To understand why this combination matters, consider what each capability does on its own:<\/p>\n\n\n\n

SharePoint grounding alone<\/strong>: The agent can search your enterprise documents and give accurate, permission-aware answers. But every conversation starts from zero. A user in the legal department who asks about compliance policies every morning has to re-establish context each time. The agent doesn’t know they’re a lawyer, doesn’t remember which policies they’ve already reviewed, and can’t adapt its response style to their preferences.<\/p>\n\n\n\n

Memory alone<\/strong>: The agent remembers who the user is and what they care about. But it can’t actually access any enterprise content. It’s a knowledgeable assistant with no access to your company’s knowledge base.<\/p>\n\n\n\n

Combined<\/strong>: Now you have something genuinely useful. The agent knows that this user works in legal, prefers detailed responses with specific clause references, and was asking about the updated data privacy policy last week. When they come back and ask “any updates on what we discussed?”, the agent can search SharePoint for recent changes to the data privacy policy and present the results in the format this particular user prefers. No context re-establishment needed.<\/p>\n\n\n\n

Prerequisites<\/h2>\n\n\n\n

This post builds on the code from Part 1. Make sure you have:<\/p>\n\n\n\n

From Part 1:<\/strong><\/p>\n\n\n\n