Agentic RAG Systems Risk Runaway Costs Without Explicit Retrieval Loop Budgets

Agentic Retrieval-Augmented Generation (RAG) systems improve on simple pipelines by iteratively rewriting queries, fetching additional context, and verifying contradictions — but this adaptability comes at a compounding cost. Unlike basic RAG, each extra loop iteration can trigger additional model token usage, reranker calls, vector database queries, and API latency, making expenses multiply quickly. The core problem is that without hard budget controls, an agent loop behaves like an unbounded search process, continuing to refine evidence even when marginal accuracy gains are negligible. Engineers are advised to set explicit limits on retrieval steps, unique queries, and context tokens, while routing straightforward questions away from full agent loops entirely. Measuring cost-adjusted accuracy — rather than raw accuracy alone — is recommended as the practical standard for evaluating production agentic RAG deployments.