An agentic RAG system can do something a simple retrieval pipeline cannot: notice missing evidence, rewrite the query, fetch more context, verify contradictions, and try again.
That is also exactly how it quietly becomes expensive.
A demo looks great. The agent decomposes the question, retrieves from three sources, reranks, reflects, retrieves again, and produces a careful answer. Then production traffic arrives. Half the questions are simple. The agent still plans, retrieves, expands, reranks, and reflects. Latency climbs. Token usage climbs. Retrieval calls multiply. The system is now paying an agentic tax on questions that a single retrieval step could have answered.
The problem is not that agentic RAG is bad. The problem is that an agent loop without budget controls behaves like an unbounded search process. It can keep improving evidence until the marginal gain is tiny, while the cost curve keeps going up.
TL;DR
