{"podcast":{"title":"The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)","slug":"twiml-ai-podcast","podcast_index_feed_id":1045879,"rss_url":"https://feeds.megaphone.fm/MLN2155636147","website_url":"https://twimlai.com","image_url":"https://megaphone.imgix.net/podcasts/35230150-ee98-11eb-ad1a-b38cbabcd053/image/TWIML_AI_Podcast_Official_Cover_Art_1400px.png?ixlib=rails-4.3.1&max-w=3000&max-h=3000&fit=crop&auto=format,compress","author":"TWIML","episode_count":785,"summary":"Machine learning and artificial intelligence are dramatically changing the way businesses operate and people live. The TWIML AI Podcast brings the top minds and ideas from the world of ML and AI to a broad and influential community of ML/AI researchers, data scientists, engineers and tech-savvy business and IT leaders. Hosted by Sam Charrington, a sought after industry analyst, speaker, commentator and thought leader. Technologies covered include machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, computer science, data science and more.","last_synced_at":null,"page_url":"https://stenobird.com/podcast/twiml-ai-podcast"},"episode":{"title":"How Capital One Delivers Multi-Agent Systems with Rashmi Shetty - #765","slug":"how-capital-one-delivers-multi-agent-systems-with-rashmi-shetty-765","published_at":"2026-04-16T23:48:00+00:00","page_url":"https://stenobird.com/podcast/twiml-ai-podcast/how-capital-one-delivers-multi-agent-systems-with-rashmi-shetty-765","show_page_url":"https://stenobird.com/podcast/twiml-ai-podcast","url":"https://twimlai.com/podcast/twimlai/how-capital-one-delivers-multi-agent-systems","audio_url":"https://pscrb.fm/rss/p/traffic.megaphone.fm/MLN9114691307.mp3?updated=1776383902","summary":"Capital One scales multi-agent systems in highly regulated environments by separating agent design from runtime governance. The discussion explores how their 'Chat Concierge' uses specialized models and robust observability to execute complex, goal-oriented actions.","meta_description":"Learn how Capital One deploys multi-agent AI systems, manages model specialization, and implements enterprise-grade governance and observability.","key_points":["Main idea: Multi-agent systems are essential for breaking down complex, multifaceted problems into specific, goal-oriented actions","Practical takeaway: Use a platform-centric approach to separate agent design from runtime governance, embedding guardrails and cyber controls at agent boundaries","Failure mode: Avoid treating agents as isolated units; instead, evaluate them using end-to-end frameworks that account for the entire stochastic workflow","Strategy: Leverage model distillation and fine-tuning to achieve the necessary balance between reasoning capabilities and low-latency performance","Technical takeaway: Implement a robust observability stack and pluggable SDKs to allow the platform to evolve alongside rapidly advancing LLM capabilities"],"chapters":[{"start_ms":65000,"title":"The Evolution of Intelligence","summary":"Rashmi discusses her transition from academic research in distributed intelligence to operationalizing agentic AI in the enterprise."},{"start_ms":310000,"title":"The Shift to Multi-Agentic Workflows","summary":"An exploration of why complex problems require moving beyond simple LLM responses toward systems capable of taking specific actions."},{"start_ms":595000,"title":"Operating in Regulated Environments","summary":"How Capital One manages the tension between deploying cutting-edge agentic technology and maintaining strict regulatory compliance."},{"start_ms":1040000,"title":"Platform Architecture and Data Lineage","summary":"The technical challenges of managing context, memory, and tool integration across multiple agents without exhausting context windows."},{"start_ms":1270000,"title":"Evaluation and Golden Datasets","summary":"Moving from individual agent evaluations to end-to-end evaluation frameworks for complex, multi-step workflows."},{"start_ms":1530000,"title":"Designing for Future Scalability","summary":"How a robust observability stack and pluggable SDKs allow for the seamless integration of new AI components and models."},{"start_ms":1785000,"title":"Model Specialization and Distillation","summary":"Using fine-tuning and student-teacher distillation to optimize for reasoning, latency, and personalized customer experiences."},{"start_ms":2755000,"title":"Treating Agentic AI as a System","summary":"Final lessons on the importance of treating agentic workflows as integrated systems rather than a collection of disparate tools."}],"topics":["Multi-Agent Systems","Generative AI Platform","Enterprise AI Governance","Model Distillation","AI Observability","LLM Evaluation","Agentic Workflows","Cloud-Native AI"],"duration_seconds":3258,"processing_state":"processed","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/twiml-ai-podcast/episodes/how-capital-one-delivers-multi-agent-systems-with-rashmi-shetty-765/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/twiml-ai-podcast/how-capital-one-delivers-multi-agent-systems-with-rashmi-shetty-765.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}