More The TWIML AI Podcast episodes
Published 16 Apr 2026
Duration: 00:54:52
Capital One's *Chat Concierge* multi-agentic AI system streamlines car-buying through self-reflection, real-time APIs, and LLM-driven workflows, addressing enterprise AI challenges like governance, scalability, and legacy system integration while prioritizing compliance, observability, and flexible platform adoption.
In this episode, Rashmi Shetty, senior director of enterprise generative AI platform at Capital One, joins us to explore how the company is designing,...
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.
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21 May 2026 Relational Foundation Models for Enterprise Data with Jure Leskovec
Relational foundation models and graph-based machine learning, like GNNs, enable accurate predictions on structured data across biomedical research and industries by capturing complex relationships, integrating multi-scale data, and overcoming traditional limitations through automated feature extraction and hybrid modeling.