As enterprises increasingly adopt autonomous agents to drive critical decision-making, the need for high-performance, transparent multi-agent systems has never been greater. This session presents a practical and forward-looking use case: deploying a multi-agent AI architecture for real-time financial market analysis and trading. We’ll explore how specialized agents responsible for data ingestion, news interpretation, risk assessment, strategy formulation, and trade execution collaborate in a dynamic, modular ecosystem. Using tools like LangGraph, Redis, and vector databases, the system balances speed, scalability, and explainability, enabling traceable decision paths and agent accountability at every step. Attendees will gain actionable insights into designing agent workflows that are not only optimized for enterprise-grade performance, but also auditable and transparent, making them suitable for regulated, high-stakes environments.