How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765

Multi-agentic AI systems represent a shift from simple LLM-based text generation to goal-oriented, autonomous action-taking within complex enterprise environments. Rashmi Shetty, Senior Director of Enterprise Generative AI Platform at Capital One, details how the organization leverages multi-agent architectures to solve multifaceted problems, such as the "Chat Concierge" application, which automates car buying tasks like financing and test drive scheduling. Success in this domain requires a risk-first platform approach, where governance, security, and observability are baked into the infrastructure rather than treated as afterthoughts. By prioritizing end-to-end latency optimization, closed-loop feedback mechanisms, and specialized model distillation, organizations can scale agentic workflows safely. These systems rely on robust data pipelines and standardized developer toolkits to bridge the gap between experimental pilots and secure, production-ready applications, ensuring that complex, probabilistic AI behaviors remain aligned with strict regulatory and business standards.