Agentic Retrieval-Augmented Generation (Agentic RAG) extends classical RAG by giving the system agency to decide when and how to retrieve information. While this can improve flexibility and accuracy, it introduces several key trade-offs:
- Latency vs. Relevance: Agentic RAG may incur higher latency because the system makes multiple retrieval decisions or calls external tools. However, this can yield more relevant and context-aware results.
- Complexity vs. Interpretability: The autonomous behavior makes the system harder to debug and monitor. Understanding why a specific retrieval was triggered becomes more challenging.
- Cost vs. Performance: Additional LLM calls or multi-step reasoning increase computational cost, but may reduce hallucination and improve grounding.
- Recall vs. Precision: Agentic RAG can improve recall by dynamically widening searches, but precision may suffer if the agent retrieves off-topic information.
Understanding these trade-offs is crucial for designing production-ready RAG systems that balance accuracy, cost, and user experience.