Despite recent breakthroughs in AI, large organizations have not yet adopted large-scale methods to align algorithmic decisions across multiple stakeholders. This paper suggests an architecture that combines large language models with multi-agent systems to address this issue. We propose a system where business KPIs can be set and aligned with algorithms, ensuring that decision-making is directly connected to organizational objectives. Furthermore, we propose a system where company values can be digitalized and aligned with algorithms, embedding the organization’s ethical standards into every decision-making process. Additionally, algorithm decision logic can be audited, providing transparency and accountability in the way decisions are made. Agentic Algorithmic Decision Alignment with RAG architecture (AADAR) represents an advancement in AI-augmented decision-making for corporate environments. AADAR enables natural language interaction between AI systems and users, facilitating adaptable decision-making tools for leaders, aligned with evolving business strategies and ethical standards. Experimental validation of AADAR within the banking sector demonstrates its effectiveness across diverse stakeholder groups, ranging from customers to business and compliance managers. This validation underscores AADAR’s potential to transform stakeholder engagement and marks a significant leap in utilizing AI for ethically sustainable business management.