Agentic RAG creates an unavoidable trade-off: more reasoning can improve evidence quality, but it can also add latency. The engineering problem is not to choose speed or accuracy once. It is to decide which path a query deserves. ## Trade-off table

Decision	Latency impact	Accuracy impact
Direct retrieval	Lower	Weaker for complex questions
Decomposition	Higher	Stronger for multi-part questions
Hybrid retrieval	Medium	Stronger for mixed semantic and lexical needs
Caching	Lower when safe	Neutral or risky if context is wrong
Clarification	Higher interaction cost	Safer when evidence is missing

Low latency is a first-class constraint, but not permission to skip grounding. ## What a good system should do A good Agentic RAG runtime should avoid expensive work when the query is simple. It should spend more effort when the query is complex. It should reuse intermediate work when context allows it. And it should stop when evidence is missing. That is the practical target: fast enough to be usable, grounded enough to be trusted, and explicit enough to debug.

Blog: Accuracy

Latency and Accuracy Trade-Offs in Agentic RAG