In this tutorial, we walk through the implementation of an Agentic Retrieval-Augmented Generation (RAG) system. We design it so that the agent does more than just retrieve documents; it actively decides when retrieval is needed, selects the best retrieval strategy, and synthesizes responses with contextual awareness. By combining embeddings, FAISS indexing, and a mock LLM,
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October 3, 2025The agentic decision-making layer tackles a real issue with basic RAG setups – they blindly fetch data for every query, even when external context isn’t necessary. I’ve tested similar logic in local environments where the model first decides if it can answer from parametric knowledge or if it needs to retrieve documents. The overhead matters: each retrieval adds latency, and with slower local models (1-5 tokens/sec on 70B with partial CPU offload), unnecessary context fetching can significantly slow responses. The multi-query comparison approach is smart but costly. Generating three different embedding queries, retrieving two documents per query, then deduplicating results means six FAISS searches instead of one.
During my local tests of different retrieval methods, the performance cost of multiple searches often outweighs the quality improvements unless you’re dealing with very large, diverse knowledge bases where single queries might miss key comparisons. The mock LLM demos gloss over implementation complexity. In real production, actual LLM calls are necessary for decision-making, introducing failure modes the tutorial doesn’t cover: what happens if the LLM hallucinates a strategy name, returns malformed output, or takes 30 seconds to decide whether to retrieve?
I’ve encountered all of these during testing of local agent workflows. While the clean prompt→parse→execute flow works in demos, it often fails with real models, especially smaller ones that struggle with structured output formatting. The temporal retrieval re-ranking depends on clean metadata, which is rarely the case. Most document stores have inconsistent or missing date fields. Building this locally, metadata validation becomes a significant engineering challenge even before the RAG logic runs.