Your AI agent is rediscovering 85% of its context every run. Here's the architecture fix (+ Contract Spec, Failure Triage, and Stack ADR)

There’s a debate going on right now about whether vector search is obsolete. That’s the wrong layer to be arguing about. The agents I’m watching fail in production aren’t failing because the retrieval method is wrong — they’re failing because the retrieval system can’t assemble what the agent actually needs before it starts acting.

A vector database can find text semantically related to a question. Useful, but nowhere near enough. Agents need the current account record, the user’s permissions, the controlling policy, the right section of a long document, the table behind a metric, the prior decision from a meeting, and the source trail that lets a human reviewer reconstruct why the agent did what it did. When the system doesn’t prepare that context, the model improvises — and the cost shows up everywhere except the place you’re looking. Wrong refunds get issued. Stale policies get cited. Outdated metrics make it into board decks. The agent burns tokens and wall-clock time rebuilding context every run, and when the answer finally lands, it lands confidently — which is the most expensive way to be wrong. That’s the new RAG problem — not a retrieval problem, an assembly problem.

So the next move isn’t “vectors versus something else.” It’s that vector search is quietly getting demoted from the whole architecture to one component inside a broader knowledge layer for agents — a layer that includes retrieval, but also document structure, semantic data models, access control, provenance, memory, and write-back. I want to be careful not to overstate this: vector search isn’t going anywhere. But the conversation about where the real work happens has moved.

Here’s what’s inside:

• Why classic RAG worked — and where it stops working. How to spot the moment your retrieval architecture became the bottleneck instead of the solution.

• What Pinecone, PageIndex, SAP, and Dremio are all saying. Four different companies, one shared shift in what “retrieval” actually means for agents.

• The practical architecture. Seven questions to test whether your knowledge layer can support a production agent.

• What could go wrong. Where this new architecture quietly breaks, and how to tell if you’re overbuilding.

• How to put this to work. A Retrieval Contract Spec, a Failure Triage, and a Stack ADR: paste-ready artifacts for the three states a builder hits when working on retrieval.

Let’s walk through how this shift is playing out across the stack, and what it means for how you build.