An advanced trading paradigm where self-directed AI agents with long-term memory, multi-step planning capabilities, and tool integration execute end-to-end trading operations across global financial markets. These agents can independently analyze market conditions, develop trading strategies, execute orders, manage risk, and adapt to changing market dynamics in real-time. Unlike traditional algorithmic trading, agentic AI trading systems can handle unstructured data sources (news, social media, earnings calls), negotiate with other agents, and learn from market outcomes to continuously improve performance. Major investment banks like Goldman Sachs and JPMorgan have deployed agentic trading systems that now account for 15-20% of their daily trading volume, with reported 25% higher risk-adjusted returns compared to traditional quantitative strategies.