Abstract
Large language models (LLMs) can produce plausible but incorrect content, a risk that is unacceptable when public institutions disclose scientific and legal information. This study presents and evaluates a hybrid agentic retrieval-augmented generation (RAG) system that automates first-level responses to citizen information requests at the Marine Institute of Peru (IMARPE). A baseline analysis of 196 requests showed a mean response time of 12 business days, above the 10-day legal limit, and identified 26.9% of them as routine, data-extractable queries. The system, developed under CRISP-DM, uses LangGraph to route each query to a LightRAG knowledge graph (Neo4j) built from institutional yearbooks, to a Text-to-SQL agent that queries the IMARSIS relational database through the Model Context Protocol (MCP), or to both, with Gemini 2.5 Flash as the foundation model and a referral protocol for out-of-scope requests. Five institutional domain experts rated 30 test cases, with mean scores of 4.45/5 for factual accuracy, 4.10/5 for completeness, and 4.57/5 for clarity. Mean end-to-end software latency was 20.43 s (maximum 49.20 s), and 30 pilot users rated usability at 80.42 on the System Usability Scale (Cronbach’s α = 0.96). These preliminary, single-site results indicate that combining graph-based and SQL-based grounding is feasible for routine citizen-information services; comparison with simpler baselines remains pending.