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Delivering judicial decisions requires interpreting complex legal texts, analyzing evidence, and reasoning over jurisprudence and legal principles. Recent advances in Generative Artificial Intelligence, particularly Large Language Models (LLMs), have shown potential to automate parts of this process; however, practical and measurable benefits in real-world judicial settings remain limited. This paper introduces SARA, an LLM-powered legal reasoning platform deployed in a regional Brazilian court, which demonstrates significant efficiency and quality gains through the integration of LLM agents with a Jurisprudential Knowledge Graph (Jur-KG). SARA automatically extracts and structures key elements from legal documents, including claims, requests, and evidence, and generates legal reasoning grounded in retrieved jurisprudential precedents. The Jur-KG is modeled through an ontology encompassing core legal concepts such as parties, facts, and legal claims, enabling semantic matching and retrieval of relevant case law. By representing cases according to the Legal Case Ontology for the Brazilian Judicial System, SARA supports traceable reasoning and addresses competence questions to assess the coverage, coherence, and justification of AI-generated outputs. Deployment results indicate measurable improvements in processing time, consistency, and explainability, while ensuring compliance with ethical and legal guidelines established by Brazil’s National Council of Justice. This work demonstrates that combining LLM-based agents with domain-specific knowledge graphs can deliver both innovative capabilities and proven impact in judicial decision-making.