How Capital One Delivers Multi-Agent Systems with Rashmi Shetty

Capital Ones Chat Concierge is a multi-agentic AI system designed to streamline the car-buying process by integrating self-reflection, layered reasoning, and real-time API checks to assist with tasks like vehicle matching, test drive scheduling, financing approval, and trade-in valuations. This system exemplifies advanced, enterprise-grade agentic AI, reflecting Capital Ones shift from traditional machine learning to large language models (LLMs) and generative AI since 2023. The multi-agentic approach addresses complex, multifaceted challenges by decomposing tasks into specialized agents, enabling autonomous decision-making while aligning with governance, compliance, and risk management frameworks. This architecture emphasizes scalability, operationalization, and integration with existing data pipelines, leveraging cloud-based governance and auto-ML advancements.

Key challenges in developing agentic systems include managing multi-layered complexity, ensuring latency and performance as critical product features, and aligning with legacy infrastructure. Observability is central to these systems, requiring real-time monitoring of agent behavior, reasoning, and feedback loops, alongside replay analysis to trace decision pathways. The platform prioritizes developer tools that balance speed, safety, and governance, offering SDKs and frameworks to accelerate deployment while ensuring compliance, risk control, and seamless integration with enterprise data. Capital Ones strategy underscores the importance of leveraging existing data governance foundations, enabling rapid iteration from experimentation to production, and fostering collaboration between developers and compliance teams to align agentic AI with regulatory and operational needs.