Capital One is building its own scalable AI architecture using customized open-weight models rather than off-the-shelf foundation models. Years of investment in data transformation and cloud adoption laid the groundwork for this strategy. The bank centralizes AI with built-in governance and deeply customizes open models with proprietary data. This approach leverages unique company data for specialized AI capabilities. It also offers extensibility, with benefits from one use case often lifting performance across the entire portfolio. A multi-agentic workflow, MACAW, is used for complex tasks like fraud detection, breaking down interactions into specialized agent roles for understanding, reasoning, validation, and explanation. This system assists hundreds of customer-service agents by automating post-call summaries for lengthy interactions. Capital One's Chat Concierge, an auto-shopping assistant, also employs this multi-agent structure with a customized version of Meta's Llama model. The company is also using agentic AI to automate rote tasks, such as optimizing backend hosting infrastructure latency and cost through an autonomous research system. Future trends include model routing for enhanced accuracy and cost-effectiveness, and a shift towards proactive, event-driven AI systems that act without explicit prompts. This strategic approach allows Capital One to achieve differentiated performance, cost, and latency goals, driving continuous innovation in financial services.