Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA

2026-03-09

AI Summary

This work addresses the persistent challenges of hallucination and inaccurate citation in large language models when responding to Islamic jurisprudence (fiqh) queries, which often fail to meet the rigorous demands for precise references to the Qur’an, Hadith, and nuanced legal reasoning. To this end, we propose the first multi-agent architecture specifically designed for Islamic question answering. The system employs intent recognition to dynamically route queries to specialized modules, integrating retrieval-augmented generation, citation validation mechanisms, and a madhhab-sensitive deterministic rule engine. This enables accurate scriptural retrieval, compliant fiqh reasoning, and precise calculations for zakat and inheritance. Evaluated on public benchmarks, the system demonstrates strong performance and has served approximately 1.9 million user requests via API and web interfaces within its first year of deployment.

Technology Category

• Natural Language Processing: Question Answering
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web question answering
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Community question answering

Abstract

Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic settings where users expect grounding in canonical texts (Qur'an and Hadith) and jurisprudential (fiqh) nuance. Retrieval-augmented generation (RAG) reduces some of these limitations by grounding generation in external evidence. However, a single ``retrieve-then-generate''pipeline is limited to deal with the diversity of Islamic queries.Users may request verbatim scripture, fatwa-style guidance with citations or rule-constrained computations such as zakat and inheritance that require strict arithmetic and legal invariants. In this work, we present a bilingual (Arabic/English) multi-agent Islamic assistant, called Fanar-Sadiq, which is a core component of the Fanar AI platform. Fanar-Sadiq routes Islamic-related queries to specialized modules within an agentic, tool-using architecture. The system supports intent-aware routing, retrieval-grounded fiqh answers with deterministic citation normalization and verification traces, exact verse lookup with quotation validation, and deterministic calculators for Sunni zakat and inheritance with madhhab-sensitive branching. We evaluate the complete end-to-end system on public Islamic QA benchmarks and demonstrate effectiveness and efficiency. Our system is currently publicly and freely accessible through API and a Web application, and has been accessed $\approx$1.9M times in less than a year.

Problem

• Islamic QA
• hallucination
• source grounding
• fiqh
• religious knowledge

Innovation

• multi-agent architecture
• retrieval-augmented generation
• intent-aware routing
• deterministic citation verification
• madhhab-sensitive computation