Agentic RAG Explained: A Hands-on Example with Dynamic Control and Validation in Basketball Data Retrieval

Agentic RAG represents an evolution of traditional Retrieval-Augmented Generation (RAG) by introducing autonomous decision-making capabilities through agent-like control mechanisms. While the core architecture remains retrieval and generation, the key enhancement lies in dynamic control—where the system can decide whether to proceed, switch paths, or terminate based on internal validation and reasoning. In this hands-on example, the scenario involves retrieving information about Yao Ming, a renowned Chinese basketball player. The system uses two data sources: one for Chinese players and another for American players. The workflow begins with indexing both datasets—40 documents from the American player dataset and 41 from the Chinese one—using spaCy for tokenization and the rank_bm25 library for BM25-based retrieval. Vector embeddings are generated via the Qwen text-embedding-v4 model. The router, powered by a Qwen-8B model, analyzes the query "who is yaoming?" and determines that only Chinese player data is relevant, returning ["china"]. The system then performs both vector and BM25 retrieval on the Chinese dataset. The vector search returns five documents, including Yao Ming’s profile, while BM25 retrieves none, indicating a potential mismatch between semantic similarity and keyword-based relevance. Next, the filter_content module evaluates whether the retrieved data is sufficient to answer the query. It combines the query with the retrieved content and checks if it can support a meaningful response. In this case, the content about Yao Ming is relevant and sufficient, so the module returns True. The system then feeds the filtered content into the reranker (gte-rerank-v2) to prioritize the most relevant snippets. Since the validation step confirms sufficiency, the system bypasses external web retrieval and directly inputs the combined data into the Qwen-14B model for response generation. The final output includes Yao Ming’s height, weight, position, and notable honors such as being a Naismith Hall of Fame inductee and an 8-time NBA All-Star. This example demonstrates how Agentic RAG enables intelligent control: the system doesn’t blindly retrieve and generate—it evaluates, validates, and only proceeds when confidence is high. If the validation had failed, the system would have initiated a fallback by retrieving new data from the web, merging it with existing results, and re-evaluating. In industrial applications, Agentic RAG faces greater challenges. Multiple data sources, complex file formats (PDF, Excel, images, Word), and strict accuracy demands require robust parsing tools like PyMuPDFLoader, Docx2txtLoader, and PillowLoader. Chunking strategies must be carefully designed to preserve context. Model accuracy across modules remains a critical bottleneck. Smaller models like Qwen-8B may lack precision, while larger models (32B+) introduce latency. Solutions include distillation, synthetic data generation with manual review, and supervised fine-tuning. However, even with high individual accuracy, the cumulative error rate increases—e.g., 0.9 × 0.9 = 0.81—highlighting the need for error mitigation. To address this, strategies such as confidence-based fallbacks (e.g., querying all sources if router confidence is low) and self-reflection modules can be implemented. These allow the system to audit its own decisions, correct errors, and rerun specific steps—though at the cost of increased latency. In conclusion, Agentic RAG is not a replacement for traditional RAG but a more intelligent, adaptive version. It enhances reliability in complex, multi-source environments by enabling dynamic control. Future improvements will focus on balancing efficiency and accuracy—through lightweight self-reflection, collaborative training of modules, and multimodal support. Developers should align their technical choices with business priorities: whether to optimize for speed, precision, or robustness—rather than adopting Agentic RAG simply for its trendiness.