Embedding similarity is not identity, and I think that is the whole RAG problem for agents

Short one, and I would like to be argued with.

RAG retrieves passages that are semantically similar to the query. That is genuinely useful for "what did we write about X" and it is the wrong primitive for "is this the same thing as that".

Similarity is continuous and identity is binary. No cosine distance threshold turns one into the other. You can tune it so that most of the time similar enough means same, and then you have a system that is quietly wrong at the edges, which is exactly where acquisitions, renames and subsidiaries live.

Concrete failure: ask a RAG system how many active contracts Northwind has. It returns three documents mentioning Northwind. Two are about Northwind Ltd, one is about Northwind Technologies, which is the post acquisition renamed entity. The system has no mechanism to decide whether those are one company or two. It will summarise all three as if they were one, or as if they were separate, depending on phrasing.

A human reading the same three documents resolves this in a second using context the embeddings do not carry.

This is why I think the Gartner prediction about graph based context is less about graph databases winning and more about retrieval alone being structurally insufficient for agents. A graph carries identity as a first class thing: this node is that node, or it is not, and the edge is explicit.

Where I might be wrong:

1. Maybe this is solvable with better entity linking in the retrieval pipeline rather than needing a graph at all. NER plus a resolution step against a canonical entity list gets you a long way and is much cheaper.

2. Maybe agents will just get good enough at asking clarifying questions that the ambiguity gets resolved at runtime, the way a human does.

Has anyone got the entity linking approach working well enough that the graph is unnecessary? That is the version I would rather be true, because it is far less work.