Caching is necessary for low-latency Agentic RAG. It is also dangerous if it ignores context. Two questions can look similar and still require different evidence. A cached result is useful only when the system can prove the retrieval state is compatible. ## Cache layers worth separating

Cache layer	What it reduces	Main risk
Fingerprint cache	Repeated equivalent work	Stale reuse
Semantic cache	Similar query handling	Context mismatch
Embedding cache	Repeated embedding calls	Input drift
Decomposition cache	Repeated planning	Wrong sub-query reuse

Every millisecond saved must preserve the grounding path. Fast ungrounded answers are not a win. ## Context isolation Context isolation is the key constraint. The cache must not treat two prompts as equivalent when the surrounding conversation changes the referent, source, or evidence requirement. ## What not to claim We do not publish cache hit-rate claims or latency numbers without measurement. The public point is the implementation boundary: caching exists to reduce repeated work, not to bypass evidence selection.

Blog: Cache

Caching in RAG Without Breaking Grounding