Access to legal information presents a significant impediment in developing nations like Bangladesh, where a substantial portion of the populace lacks legal literacy and access to professional counsel. This paper introduces a Multi-Agent Retrieval Augmented Generation (RAG) system specifically designed for Bangladeshi legal information retrieval. The system employs a novel two-agent architecture: a Clarification Agent that refines user queries through Large Language Model (LLM)-driven clarity assessment and interactive questioning, and a primary RAG Agent. The RAG Agent executes a comprehensive process involving document retrieval from a meticulously curated legal corpus, relevance grading, conditional web search, and final answer synthesis. This corpus, compiled from official government websites, legal blogs, and scholarly articles, ensures accurate representation of the Bangladeshi legal landscape. The multi-agent RAG pipeline, through structured collaboration between these autonomous agents, is designed to improve the relevance and contextual accuracy of responses to complex legal queries. A conversational interface built with Streamlit facilitates accessible, real-time user interaction. The Agentic RAG model outperforms baseline and general-purpose legal bots (using GPT-4o-mini and Gemma 3B) on 20 diverse legal queries, showing better contextual relevance, accuracy, and fewer hallucinations. It also supports cost-effective local deployment via the Ollama framework. This research demonstrates a scalable and potentially cost-effective approach to democratizing legal knowledge, which is particularly vital in low-resource settings such as Bangladesh.