Agentic Rag for Version-controlled Documentation: A Pdca-driven Navigation and Semantic Comparison Framework

Retrieval-augmented generation (RAG) improves factual grounding by conditioning generation on external text, yet common deployments remain vulnerable in domains where documentation evolves rapidly. Two failure modes are particularly disruptive: temporal blindness, where retrieval mixes incompatible versions of the same manual, and passive retrieval, where returned snippets omit navigational context needed for efficient verification. This paper presents DocNavigator, an agentic framework that elevates RAG from static snippet delivery to goal-directed navigation and version-conditioned semantic comparison. A Version-Aware Vector Space partitions embeddings by explicit release metadata and supports cross-version alignment for concept-level change detection. A PDCA (Plan-Do-Check-Act) agent loop evaluates retrieval relevance before generation, applies reflective confidence signals to detect stale or mismatched evidence, and triggers just-in-time acquisition when indexed content is incomplete. In place of purely textual answers, a deep-link navigator returns actionable browser commands composed of URL targets and scroll anchors, enabling immediate inspection at the source. A prototype evaluation on versioned documentation tasks indicates meaningful reductions in time-to-insight and a marked decrease in hallucinations associated with deprecated interfaces, highlighting the value of combining navigation actions, version isolation, and closed-loop correction in documentation-centric RAG systems.