How Capital One Delivers Multi-Agent Systems with Rashmi Shetty

Q: Chat Concierge

处理意图解析、工具调用和人工交接。

Q: 平台方法

分离设计与运行时治理。

Q: 团队关注点

开发者体验、可观测性和评估。

TWIML AI Podcast

TWIML AI Podcast播客2026年4月16日54:18

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

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时长 54:18原播客页面

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会先在本集摘要、章节、转录和笔记里找答案。

TL;DR · AI 摘要

Capital One通过Chat Concierge多代理聊天系统展示了如何在受监管环境中设计、部署和扩展多代理系统。

核心要点

Chat Concierge处理意图解析、工具调用和人工交接。
平台方法分离设计与运行时治理。
团队关注开发者体验、可观测性和评估。

结构提纲

按章节快速跳转。

§关于本集
Rashmi Shetty讨论了Capital One如何设计、部署和扩展多代理系统。
·Chat Concierge
处理意图解析、工具调用和人工交接。
·平台方法
分离设计与运行时治理。
·团队关注点
开发者体验、可观测性和评估。

思维导图

用一张图看清主题之间的关系。

查看大纲文本（无障碍 / 无 JS 友好）

Capital One的多代理系统

金句 / Highlights

值得收藏与分享的关键句。

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 e
— 第 2 段
⬇︎ 下载 PNG 𝕏 分享到 X
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.
— 第 2 段
⬇︎ 下载 PNG 𝕏 分享到 X
Rashmi shares how the team approaches the developer experience for agent builders, observability, and evals for stochastic, multi-agent workflows;
— 第 2 段
⬇︎ 下载 PNG 𝕏 分享到 X

章节

要点
Chat Concierge处理意图解析、工具调用和人工交接。
Chat Concierge处理意图解析、工具调用和人工交接。
要点
平台方法分离设计与运行时治理。
平台方法分离设计与运行时治理。
要点
团队关注开发者体验、可观测性和评估。
团队关注开发者体验、可观测性和评估。

转录

这期还没有可搜索转录。后续抓到带时间戳的内容后会自动补到这里。

#Capital One#Multi-Agent Systems#AI

节目笔记

How Capital One Delivers Multi-Agent Systems | TWIML - The Voice of Machine Learning & AI

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EPISODE 765

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APRIL 16, 2026

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About this Episode

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.

About the Guest

#### Rashmi Shetty Capital One

Connect with Rashmi

Thanks to our sponsor Capital One

Capital One’s tech team isn’t just talking about multi-agentic AI, they already deployed one. It’s called Chat Concierge, and it’s simplifying car shopping. Using self-reflection and layered reasoning with live API checks, it doesn’t just help buyers find a car they love, it helps schedule a test drive, get pre-approved for financing, and estimate trade-in value. Advanced, intuitive, and deployed: that’s how they stack. That’s technology at Capital One. To learn more about AI at Capital One, visit capitalone.com/tech/ai/.