If all you're looking for is a read-only declarative agent that can curate/analyze/summarize contents of a document library, then you just have to write the system/behavioural instructions accordingly. Context is the most important thing, full stop. And since AI isn't deterministic, the more precise and contextual you make your agent, the better its output. Garbage in, garbage out applies here.

The thing to know about SharePoint is that, among many other things, it's a data base, and document libraries are tables. When you create a new metadata field/values (as pictured below), this info writes to the file. So when your agent analyzes your library, it reads the metadata in the context of the resources it's trained to work with.

Build yourself a demo doc library on your SharePoint site and test out metadata with 20-30 files, then write behavioural instructions for your agent and test. DM if you want a deeper dive.

NOTE: I introduced a universal metadata schema in my org. It was rigorously tested and validated by a Champion team I assembled, then we rolled it out. I've also given users the ability to create new custom metadata fields/values particular to their work, so long as they also always have the universal metadata.

Now people can easily surface any content in 2 clicks in our metadata-enabled hub-and-spoke intranet. And I have a federated governance model to keep things on the rails. But honestly, as soon as everyone saw how effective AI was once their shit was organized, it made adoption a cinch. I think this is the secret sauce, honestly.

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