Standard RAG is a search engine. It finds snippets that look like your question. But intelligence isn't about what looks related — it's about what's actually connected. When the answer requires following a chain of logic, similarity alone gets you lost.
Ask a standard RAG system: "What language does the developer who likes dark mode use?" It will return a snippet about "Sarah likes dark mode" and a snippet about "Project X uses Rust." But unless those facts appear in the same paragraph, it won't connect them. Sarah is the lead on Project X. Project X uses Rust. Sarah therefore uses Rust. RAG misses this — not because it's stupid, but because it's a search engine, not a reasoner.
This is the "Relationship Needle" problem. The answer exists in your memory. But it requires multi-hop traversal — following a chain of logical connections rather than returning a single similar snippet. Standard RAG fails at this by design.
Standard RAG is not useless. For direct, single-hop questions — "What's our deployment command?" — similarity search is fast, cheap, and accurate. The failure mode is specific: multi-hop questions where the answer requires connecting facts that don't appear close together in embedding space.
In practice, most non-trivial agent tasks require multi-hop reasoning. "Should I use the same approach we used for the auth system?" requires: (1) finding what approach was used for auth, (2) understanding why it was chosen, (3) evaluating whether those same conditions apply now. RAG might return the auth code. It won't return the reasoning behind it.
VEKTOR's causal and entity layers store exactly this: the reasoning behind decisions, not just the decisions themselves. Graph traversal follows the causal chain. The agent doesn't just know what was done — it knows why, and can apply that logic to new situations.
RAG query: "What should I know about the payment system?" — Returns 5 snippets about Stripe, subscriptions, webhook handling, all of similar embedding distance. The agent reads them in order. No structure. No causality. No understanding of which constraints led to which decisions.
VEKTOR query: Same question — Traverses the entity node "payment-system." From there: causal edges to "chose Stripe because…" nodes. Temporal edges to the sequence of implementation decisions. Entity edges to related services. The agent receives a logical narrative — not a pile of similar snippets.