From DevOps ‘Heart Attacks’ to AI-Powered Diagnostics With Traversal’s AI Agents
Anish Agarwal and Raj Agrawal, co-founders of Traversal, are transforming how enterprises handle critical system failures. Their AI agents can perform root cause analysis in 2-4 minutes instead of the hours typically spent by teams of engineers scrambling in Slack channels. Drawing from their academic research in causal inference and gene regulatory networks, they’ve built agents that systematically traverse complex dependency maps to identify the smoking gun logs and problematic code changes. As AI-generated code becomes more prevalent, Traversal addresses a growing challenge: debugging systems where humans didn’t write the original code, making AI-powered troubleshooting essential for maintaining reliable software at scale.
Hosted by Sonya Huang and Bogomil Balkansky, Sequoia Capital
Mentioned in this episode:
SRE: Site reliability engineering. The function within engineering teams that monitors and improves the availability and performance of software systems and services.
Golden signals: four key metrics used by Site Reliability Engineers (SREs) to monitor the health and performance of IT systems: latency, traffic, errors and saturation.
MELT data: Metrics, events, log, and traces. A framework for observability.
The Bitter Lesson: Another mention of Nobel Prize winner Rich Sutton’s influential post.
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40:32
The Breakthroughs Needed for AGI Have Already Been Made: OpenAI Former Research Head Bob McGrew
As OpenAI's former Head of Research, Bob McGrew witnessed the company's evolution from GPT-3’s breakthrough to today's reasoning models. He argues that there are three legs of the stool for AGI—Transformers, scaled pre-training, and reasoning—and that the fundamentals that will shape the next decade-plus are already in place. He thinks 2025 will be defined by reasoning while pre-training hits diminishing returns. Bob discusses why the agent economy will price services at compute costs due to near-infinite supply, fundamentally disrupting industries like law and medicine, and how his children use ChatGPT to spark curiosity and agency. From robotics breakthroughs to managing brilliant researchers, Bob offers a unique perspective on AI’s trajectory and where startups can still find defensible opportunities.
Hosted by: Stephanie Zhan and Sonya Huang, Sequoia Capital
Mentioned in this episode:
Solving Rubik’s Cube with a robot hand: OpenAI’s original robotics research
Computer Use and Operator: Anthropic and OpenAI reasoning breakthroughs that originated with OpenAi researchers
Skild and Physical Intelligence: Robotics-oriented companies Bob sees as well-positioned now
Distyl: AI company founded by ex-Palintir alums to create enterprise workflows driven by proprietary data
Member of the technical staff: Title at OpenAI designed to break down barriers between AI researchers and engineers
Howie.ai: Scheduling app that Bob uses
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48:51
OpenAI Codex Team: From Coding Autocomplete to Asynchronous Autonomous Agents
Hanson Wang and Alexander Embiricos from OpenAI's Codex team discuss their latest AI coding agent that works independently in its own environment for up to 30 minutes, generating full pull requests from simple task descriptions. They explain how they trained the model beyond competitive programming to match real-world software engineering needs, the shift from pairing with AI to delegating to autonomous agents, and their vision for a future where the majority of code is written by agents working on their own computers. The conversation covers the technical challenges of long-running inference, the importance of creating realistic training environments, and how developers are already using Codex to fix bugs and implement features at OpenAI.
Hosted by Sonya Huang and Lauren Reeder, Sequoia Capital
Mentioned in this episode:
The Culture: Sci-Fi series by Iain Banks portraying an optimistic view of AI
The Bitter Lesson: Influential paper by Rich Sutton on the importance of scale as a strategic unlock for AI.
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37:44
Google I/O Afterparty: The Future of Human-AI Collaboration, From Veo to Mariner
Fresh off impressive releases at Google’s I/O event, three Google Labs leaders explain how they’re reimagining creative tools and productivity workflows. Thomas Iljic details how video generation is merging filmmaking with gaming through generative AI cameras and world-building interfaces in Whisk and Veo. Jaclyn Konzelmann demonstrates how Project Mariner evolved from a disruptive browser takeover to an intelligent background assistant that remembers context across multiple tasks. Simon Tokumine reveals NotebookLM’s expansion beyond viral audio overviews into a comprehensive platform for transforming information into personalized formats. The conversation explores the shift from prompting to showing and telling, the economics of AI-powered e-commerce, and why being “too early” has become Google Labs’ biggest challenge and advantage.
Hosted by Sonya Huang, Sequoia Capital
00:00 Introduction
02:12 Google's AI models and public perception
04:18 Google's history in image and video generation
06:45 Where Whisk and Flow fit
10:30 How close are we to having the ideal tool for the craft?
13:05 Where do the movie and game worlds start to merge?
16:25 Introduction to Project Mariner
17:15 How Mariner works
22:34 Mariner user behaviors
27:07 Temporary tattoos and URL memory
27:53 Project Mariner's future
29:26 Agent capabilities and use cases
31:09 E-commerce and agent interaction
35:03 Notebook LM evolution
48:26 Predictions and future of AI
Mentioned in this episode:
Whisk: Image and video generation app for consumers
Flow: AI-powered filmmaking with new Veo 3 model
Project Mariner: research prototype exploring the future of human-agent interaction, starting with browsers
NotebookLM: tool for understanding and engaging with complex information including Audio Overviews and now a mobile app
Shop with AI Mode: Shopping app with a virtual try-on tool based on your own photos
Stitch: New prompt-based interface to design UI for mobile and web applications.
ControlNet paper: Outlined an architecture for adding conditional language to direct the outputs of image generation with diffusion models
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53:51
From Data Centers to Dyson Spheres: P-1 AI's Path to Hardware Engineering AGI
Former Airbus CTO Paul Eremenko shares his vision for bringing AI to physical engineering, starting with Archie—an AI agent that works alongside human engineers. P-1 AI is tackling the challenge of generating synthetic training data to teach AI systems about complex physical systems, from data center cooling to aircraft design and beyond. Eremenko explains how Archie breaks down engineering tasks into primitive operations and uses a federated approach combining multiple AI models. The goal is to progress from entry-level engineering capabilities to eventually achieving engineering AGI that can design things humans cannot.
Hosted by Sonya Huang and Pat Grady, Sequoia Capital
Join us as we train our neural nets on the theme of the century: AI. Sonya Huang, Pat Grady and more Sequoia Capital partners host conversations with leading AI builders and researchers to ask critical questions and develop a deeper understanding of the evolving technologies—and their implications for technology, business and society.
The content of this podcast does not constitute investment advice, an offer to provide investment advisory services, or an offer to sell or solicitation of an offer to buy an interest in any investment fund.
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