- Developers
- Learn practical strategies for building, deploying, and governing complex multi-agent AI systems in production.
- Business
- Understand how enterprises can leverage AI agents for customer journeys while ensuring safety and compliance.
This is a solid interview with an enterprise leader discussing practical, real-world applications and challenges of advanced AI agent systems.
How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765
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From Subvolts
Summary and analysis by Subvolts editorial, AI-assisted.
Rashmi Shetty from Capital One discusses delivering multi-agent systems in a regulated environment on The TWIML AI Podcast. The episode covers Chat Concierge, a multi-agent system for auto dealerships, and Capital One's platform approach. It details agent design, runtime governance, developer experience, observability, and model specialization strategies.
Imagine building smart AI assistants that work together to help customers, like in a car dealership. This episode explains how Capital One creates these systems safely, managing how they work and ensuring they follow rules, making customer service better.
This episode offers insights into building and deploying complex AI agent systems within a large, regulated enterprise. It's for AI engineers, platform architects, and technical leaders interested in practical applications of multi-agent systems. The discussion on governance, safety, and developer experience is relevant as frontier labs like Google Gemini and OpenAI explore more sophisticated agentic capabilities.
- HostSam Charrington
- GuestRashmi Shettysenior director of enterprise generative AI platform, Capital One
- Guest: Rashmi Shetty, senior director of enterprise generative AI platform at Capital One
- Title: How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765
- Show: The TWIML AI Podcast
- Length: 54 minutes
- Product Mentioned: Chat Concierge
In this episode, Rashmi Shetty, senior director of enterprise generative AI platform at Capital One, joins us to explore how the company is designing, deploying, and scaling multi-agent systems in a highly regulated environment. Rashmi walks us through Chat Concierge, a multi-agent chat experience for auto dealerships that handles intent disambiguation, tool invocation, and human handoffs to deliver safer, more personalized customer journeys. We discuss Capital One’s platform-centric approach to AI agents and how it separates design from runtime governance, embedding policies, guardrails, and cyber controls across agent threat boundaries. Rashmi shares how the team approaches the developer experience for agent builders, observability, and evals for stochastic, multi-agent workflows; and strategies for model specialization, including fine-tuning and distillation. We also cover standards and abstraction, closed-loop learning from production telemetry, and key lessons for enterprises building agentic systems.
The complete show notes for this episode can be found at https://twimlai.com/go/765.
- intent disambiguation
- Rashmi Shetty
- figuring out what a user actually wants when they ask something unclear.
- tool invocation
- Rashmi Shetty
- when an AI system uses other software or data sources to get information or complete a task.
- stochastic
- Rashmi Shetty
- involving randomness or probability, making outcomes unpredictable.
- telemetry
- Rashmi Shetty
- data collected automatically about how a system is performing.
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Questions people ask
- What is Chat Concierge?
- Chat Concierge is a multi-agent chat experience for auto dealerships that handles intent disambiguation, tool invocation, and human handoffs.
- How does Capital One manage AI agents in a regulated environment?
- Capital One uses a platform-centric approach that separates design from runtime governance, embedding policies, guardrails, and cyber controls across agent threat boundaries.
Page generated Oct 19, 2026. Summaries are Subvolts' own; the audio, artwork and notes belong to Sam Charrington.

