Agentic RAGは従来のRAGの課題をどう解決するか？｜クラウドテクノロジーブログ｜ソフトバンク https://www. yayafa.com/2777654/ # Agent
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| Source: Mastodon | Original article
SoftBank’s Cloud Technology Blog unveiled a new “Agentic RAG” framework that promises to overcome the most persistent shortcomings of conventional Retrieval‑Augmented Generation. The announcement details a joint effort between SoftBank and U.S. start‑up Archaea AI to commercialise the Agentic RAG‑powered knowledge platform “Krugle Biblio” in Japan, positioning it as the first native‑language, agent‑centric solution for enterprise search and generation.
Traditional RAG pipelines stitch a static retriever to a large language model, but they still suffer from stale indexes, hallucinated outputs and an inability to orchestrate multi‑step reasoning. Agentic RAG injects an autonomous “agent layer” that can plan retrieval strategies, evaluate source credibility, and iteratively refine prompts based on self‑reflection. The blog cites internal tests where the system reduced factual errors by roughly 40 % and cut query‑to‑answer latency by half compared with SoftBank’s own Vertex AI RAGEngine.
The development matters because it bridges the gap between ad‑hoc chat interfaces and production‑grade knowledge work. Enterprises that have been wary of LLM hallucinations can now embed a self‑checking loop that dynamically pulls the latest documents, applies domain‑specific policies, and even triggers external tools such as calculators or code interpreters. For Nordic firms grappling with strict data‑sovereignty rules, a locally hosted, agent‑driven RAG could become a viable alternative to cloud‑only offerings.
What to watch next: SoftBank plans a pilot rollout with several Japanese financial institutions in Q3, while a beta for European partners is slated for early 2027. Analysts will be tracking performance benchmarks against Google’s RAGEngine and the uptake of the Krugle API in the Nordic AI marketplace. The rollout will also test how well the self‑reflection mechanisms scale when agents handle heterogeneous, multilingual corpora—a key hurdle for broader adoption.
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