Forward Deployed

Evals - Beyond the Vibe Check | LangChain, Langfuse, Mercor, CoreWeave & Galileo

Everyone says they do evals. Almost nobody does. LangChain actually put a number on it: around 89% of teams have observability set up, and only about a third ever do anything with what it collects. So I got five people who do this for a living in a room, from LangChain, Mercor, Galileo, Langfuse, and CoreWeave. Side note, three of those companies got acquired in the past year. Galileo to Cisco, Langfuse to ClickHouse, Weights & Biases to CoreWeave. The layer is getting bought up faster than most teams can figure out how to use it. A few things I didn't expect going in. Grading an agent while it runs can cost you more than the agent does. The model you're using as a judge is probably wrong in ways you'll never notice. And the guy whose company sells eval tooling told the room to stop writing evals before shipping, just put it out and learn from what breaks. We also got into the parts nobody blogs about. How many traces a person still has to read by hand. Why the same agent is a weekend project internally and a year of work at a bank. What happens when your users start doing things you never thought to test. If you've shipped an agent and had no real way to know whether it was working, this one's worth your time! Chapters 00:00 Welcome 00:34 Meet the Panel 02:11 Why Nobody Runs Evals 03:35 Traces Explained 04:14 Offline Evals Basics 05:13 Online Evals in Production 08:26 Surprises From Production 11:22 Trajectory Checks and Bucketing 12:51 Verifiers and Trusting Evals 14:21 Hard Lessons at Scale 19:10 Human Review and Judge Drift 21:29 Which Agents Are Hardest 23:41 Voice and Multimodal Evals 27:10 Cheap Binary Guardrails 30:09 Enterprise Deployment Lifecycle 35:24 Tooling and Expert Knowledge 37:40 Fixing Long Horizon Agents 40:12 Error Analysis Workflow 41:11 Trajectory Evals and Runbooks 42:27 Condensing Long Traces 43:09 Real Long Running Agents 44:11 What's Hardest to Evaluate 45:06 Verifying Tool Side Effects 46:20 Evaluating Auto Research 48:20 Picking a Sampling Rate 49:59 Metric Drift and Security 53:49 Common Evals Mistakes 57:34 Regulated Industry Rollouts 01:01:37 Sourcing Domain Experts 01:03:50 Hallucination Evaluators 01:05:37 Predictions for Evals 01:11:55 Audience Q&A Begins 01:16:30 Are Complex Playbooks Ready 01:18:22 Where Models Fall Short 01:20:04 Extracting Expert Knowledge 01:22:10 Evaluating Writing Style 01:23:24 Closing Remarks GUESTS Liam Bush - Deployed Engineer @ LangChain Braden Holstege - VP Enterprise AI @ Mercor Soumya Mohan - Head of Product @ Galileo Lotte Verheyden - Head of Developer Relations @ Langfuse Emmanuel Turlay - Director of Engineering @ CoreWeave

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The State of Computer Use Agents | Anthropic, Browser Use & KERNEL

Bot traffic on the internet just passed human traffic, two years ahead of forecast. Most of it is agents clicking through websites built for people. So I got three of the people building those agents in a room: Lucas Gonzalez Pagliere, who works on computer use at Anthropic, the team that shipped the first computer use model back in 2024. Reagan Hsu, founding engineer at Browser Use, whose open source library is sitting at around 100K GitHub stars. And Eric Feng, founding customer engineer at KERNEL, which runs the browser infrastructure underneath a lot of this - he was also first GTM at Sentry. We covered where the models genuinely are today vs where the benchmarks say they are, what it costs to run an agent long enough to finish real work, and the arms race between agents and the anti-bot systems trying to keep them out. Then the harder question underneath all of it: whether the web reorganizes itself around agents, or hardens against them. They disagreed on plenty of it. If you want to know what agents can actually pull off on a real website today - and what still stops them cold - this one's worth your time! Chapters 00:00 Welcome and Guests 00:49 Companies and Stacks 01:14 Everyday Agent Use Cases 03:38 Defining Computer Use Agents 05:15 How Computer Use Works 08:41 Screenshots vs DOM Hybrid 11:03 Benchmarks and OSWorld 13:51 OSWorld 2 Difficulty Jump 15:43 Training Models and Cost 18:57 Speed Infrastructure and Stealth 21:55 Anti Bot and KYC Future 27:24 Reverse Engineering vs UI Automation 29:43 Computer Use vs Browser Use 31:05 Scaling Laws and Harnesses 32:49 Playwright Selenium Still Matter 33:17 Playwright Still Dominates 33:27 Why Run 1000 Agents 34:39 Long Running Agent Challenges 35:45 Memory and Compaction 38:10 State Changes Mid Task 39:36 OS and Browser Fingerprints 40:42 DOM Efficiency and WebMCP 41:36 Recsys and Agent Personas 43:57 Agent Friendly Websites 45:41 Human Speed vs Agent Power 48:32 Context Window Tradeoffs 50:34 Harnesses for Temporal State 51:59 Speed Optimizations and Tabs 54:23 Human Collaboration Limits 58:09 Raw Capability vs Better APIs 01:00:47 Training Methods and Bottlenecks 01:01:41 End State Interfaces 01:05:09 Next 12 Months Predictions 01:06:17 Closing Thanks

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Spencer Whitman - Gray Swan AI's $200M Plan to Secure AI Systems

GPT-5.6 Sol goes rogue and breaches Huggingface. Washington suspends Mythos access within days over national security concerns, and the White House just held an emergency meeting to finalize a classified cybersecurity framework for frontier AI models. AI security went from niche concern to front-page hysteria basically overnight. So I sat down with Spencer Whitman, who recently joined Gray Swan AI as CPO on the back of their $40M Series A. Before Gray Swan, he founded Meta's Llama security team to stop bad actors from jailbreaking their models - he's been on the frontlines of LLM security since the beginning. We get into how Meta pressure-tested Llama for maximum harm before every open source release, why Gray Swan's attack agent has never met an AI system it couldn't break, and the AI Twitter bot that got drained of $200K in crypto in 15 minutes. Spencer also shares his (admittedly speculative) read on whether Meta gave up on the frontier before Alexandr Wang showed up, why anyone can be a hacker now, and how 15,000 red teamers are breaking models before they ever ship. If you want to understand how AI systems actually get broken - and defended - this one's worth your time! 00:00 Intro 01:01 Meet Spencer Whitman (Gray Swan CPO) 02:57 The CMU Research Behind Gray Swan 06:20 The Universal Jailbreak That Broke Every Model 06:59 How Models Learn to Refuse 13:28 Why Open Models Need Guardrails 17:57 AI Security vs. Cybersecurity 23:13 What Reasoning Models Changed 28:57 The Arena: 15,000 Red Teamers 32:16 The Agent That Deleted a Production Database 34:45 Why You Can't Just Patch an AI 38:57 There's No S in MCP 40:27 Securing Agent Protocols 42:56 Nobody Reviews AI Code Anymore 46:03 AI vs. Human Hackers 48:28 The $200K Crypto Bot Heist 53:00 How Meta Pressure-Tested Llama 58:34 Prompt Guard and Code Shield 01:01:45 Can You Trust Chinese Models? 01:07:26 Did Meta Give Up on the Frontier? 01:11:06 What Should Keep CISOs Up at Night 01:14:24 The Open Source Routing Future 01:16:47 Back to On-Prem?

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Tony Gentilcore - Glean, the $7.2B Startup Sam Altman Warned Investors About

Earlier this year, the "SaaSpocalypse" wiped out something like $2 trillion of SaaS market cap in a matter of weeks — so I sat down with Tony Gentilcore, co-founder of Glean and formerly one of the minds behind Google Search and Chrome, to figure out what's actually happening to software in the agent era. We get into a lot: why Tony thinks outcome-based pricing (the model Sierra and Decagon are famous for) won't survive, and why companies will drift back toward per-seat. Why the "no Chinese models" rule every enterprise swears by tends to evaporate the moment finance sees the token bill — and why Nemotron, GLM, and Kimi are already good enough to matter. The story behind Sam Altman reportedly telling VCs that if they backed Glean, OpenAI didn't want them as investors (Tony's reaction: "we took it as very flattering"). We also dig into the messier reality of AI at work — how it's saving employees around 11 hours a week while quietly costing them 6 back in what Tony calls "bot sitting and bot shitting," why hard token caps on engineers don't change behavior, how CTOs are blowing through their annual token budgets a quarter into the year, and why the roles of product manager, designer, and engineer are collapsing into one. If you care about where software, pricing, and enterprise AI are all heading, this one's worth your time. Chapters: 00:00 Welcome and Setup 00:56 Glean Origin Story 03:08 Enterprise Search Signals 05:07 LLMs Inflection Point 08:08 Early Product Workflow 09:45 Search Evals and Privacy 12:05 From Chatbots to Agents 13:45 Agent Use Cases 17:08 Lessons and Puck Direction 19:08 Bot sitting, bot shitting, and slop 24:44 Token Budgets and ROI 30:59 AI Trends and Moats 35:03 Training and Fine Tuning 38:03 Why Enterprises Fear China Models 39:58 Sovereignty Backlash Watch 40:26 The time Altman called out Glean 41:19 Org Roles Become Builders 44:10 Future Work Voice First 48:17 Vibe Coding vs Quality 52:20 SaaSpocalypse Evolution 54:19 Pricing Tokens Win 55:37 Outcome Pricing Doubts 57:02 Audience AI Review Overload 58:53 Subsidies and Model Choice 01:01:18 Context Layer Interoperability 01:04:52 Build vs. buy: rolling your own Glean 01:07:47 Shadow AI and the coming security mess 01:12:22 Who actually competes 01:13:36 Wrap-up and thanks

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Russ Salakhutdinov - Kimi K3 CEO’s PhD Advisor Predicts the Future of AI Agents

Kimi K3 took the world by storm last week for open-sourcing frontier level intelligence, so I sat down with Zhilin Yang's (Kimi CEO) PhD advisor Russ Salakhutdinov to talk. Russ has been everywhere in modern AI. He did his PhD with Geoff Hinton back when neural nets were a punchline, sold his startup to Apple and worked on Project Titan, teaches at Carnegie Mellon, and spent the last couple years at Meta Superintelligence Lab building computer use agents. Now he's the founder of Sooth Labs, building AI that forecasts the future. We talked about why there's no secret architecture inside the frontier labs and why the real moat is data, engineering, and infrastructure. He explains why Cursor and half the startups you know are quietly running on Chinese open source models, why all the LLMs are going to be commodities, and why the people actually building AGI don't buy the two-year timeline. We get into his time at Meta, why computer use agents still hit 60% when you need 99.9%, whether AI can beat prediction markets, and why the RL environment business isn't sticky. And he makes the case that AI should replace McKinsey, Bain, and BCG. Chapters: 0:00 - Intro 1:26 - Bumping into Hinton on the street 3:25 - When neural nets were the third choice 5:21 - Generating digits before it was cool 6:59 - AlexNet breaks computer vision 10:18 - Teaching models to describe what they see 12:10 - Early text-to-image (and the toilet seat that beat Google) 16:52 - Hallucination is a feature 19:36 - Selling Perceptual Machines to Apple 22:45 - Self-driving: 0 to 80 in a year, stuck for 5 30:30 - Inside FSD and Waymo's architecture 34:18 - Building Visual Web Arena at CMU 39:07 - Why he joined Meta Superintelligence 40:09 - The agent that plans your faculty job hunt 42:00 - Paying people for their browser history 42:45 - The coupon-hunting agent 43:35 - Why agents still fail 46:14 - 60% when you need 99.9% 47:04 - Agents on your phone 50:12 - No secret architecture at the frontier labs 51:20 - Why coding and math got solved first 54:01 - Models that smell and touch 56:14 - The future of software engineering 59:41 - Founding Sooth Labs 1:00:07 - The 13% graduation prediction 1:05:55 - Why ChatGPT can't forecast 1:08:12 - Agents first, decision systems next 1:09:23 - The Wikipedia contamination story 1:13:43 - Can AI beat prediction markets? 1:16:26 - AI replaces McKinsey 1:18:20 - Why the crowd is hard to beat 1:19:30 - China's open source models rise 1:24:26 - Why the US needs its own open models 1:25:53 - RL environment businesses won't last 1:28:35 - The end of SaaS, LLMs as commodities 1:32:55 - What's next: self-improvement, forecasting, robots 1:35:34 - The only useful robot is the Roomba 1:37:53 - What he'd study in college today 1:41:20 - Adapt or get left behind 1:44:07 - Wrapping up

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Andy Hock - How Cerebras Plans to Kill Nvidia

Cerebras IPO'd just a couple months ago and has already locked in a 750MW compute deal with OpenAI. Andy Hock's pitch: the GPU is the wrong chip for where AI is going. I sat down with Andy Hock, Chief Strategy Officer at Cerebras, whose chips are the size of dinner plates instead of postage stamps — which lets them run inference up to 15x faster than even the latest Nvidia GPUs. We got into how that architecture works, the 750MW OpenAI deal (the 2x-faster Codex option runs on Cerebras), and why the memory crisis spiking GPU prices actually benefits Cerebras, since all their memory sits on the chip. Andy also argued the AI buildout isn't a bubble, that "training is a cost center and inference is where you make the big bucks," and why that "95% of enterprise AI pilots fail" stat measured the wrong thing at the wrong time — plus their supercomputer work with governments like the UAE, and why they turned down selling chips to China. We covered all that and much more. Subscribe for more on AI and the infrastructure behind it, and follow me, Basil Chatha, for everything AI agents. Chapters 00:00 Intro 01:06 What Cerebras Actually Builds 03:41 Before LLMs Existed 06:09 The GPT Wake-Up Call 10:45 First Principles of AI Compute 15:40 One Giant Chip 18:43 So Why Do We Still Use GPUs? 20:59 Why Inference Took Over 22:27 Where Fast Tokens Win 25:07 Is AI Infra a Bubble? 29:56 The Memory Shortage, Explained 32:35 The Energy Problem 35:34 Rolling Their Own Data Centers 38:20 What a Chip Actually Costs 40:18 AI Designing AI Chips 41:40 The Supply Chain Reality 43:45 Cheap Tokens vs Fast Tokens 46:11 Why Every Millisecond Matters 48:56 Inside the OpenAI Deal 51:56 The Gigawatt Future 54:00 Selling to the Government 58:36 Exporting the US AI Stack 01:03:17 Should We Sell Chips to China? 01:06:00 Why Europe Is Falling Behind 01:07:59 Why Enterprise AI Moves Slow 01:13:05 "I Haven't Read Code in Months" 01:14:32 The Next Cerebras Chip 01:16:50 Closing Thoughts

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Leo Mehr - Ramp’s $44B Bet on Services

The hardest part of shipping an AI agent isn't the agent. It's getting it access to data buried across a dozen internal systems, and capturing the tribal knowledge that runs a company but was never written down. I sat down with Leo Mehr, Director of Engineering at Ramp (a $44B-valued company), who runs the forward deployed engineering team that walks into large companies and replaces real, painful workflows with agents. We got into why the model is usually the easy part, and why data access is "the longest pole in the tent." Leo also made the case that nobody wants to buy another piece of software anymore — every B2B company is about to become either an agent-friendly API or white-glove service for everyone, and the middle dies. We talked about whether a services business can actually be venture-scale, why he thinks the AI labs won't eat every startup (it comes down to incentives, not better models), why the biggest customers are often the worst ones to build for, and how the traits that made a great engineer ten years ago aren't the ones that matter now. We talked about all of that and a lot more. You don't wanna miss this one! Chapters 00:00 Intro 01:11 Meet Leo Maier 01:46 Building Ramp FDE 02:43 Why FDE Exists 04:23 Early Mandate Fires 06:07 FDE and Core Engineering 07:48 Enterprise Bet Pays 11:10 Ramp Monetization 16:05 Services Are the New Software 18:11 AI Agents and APIs 23:11 Pricing for Outcomes 26:35 Human Labor TAM 32:50 VCs and Rollups 36:50 Common AI Workflow Pain 38:04 Agent Data Context 38:55 Go-to-Market Motion 40:36 Customer Engagement Lifecycle 43:51 Finance Intelligence Layer 47:20 Who You Compete Against 48:09 The Future of Consulting 51:44 Will Humans Still Code? 54:37 How Engineering Teams Will Evolve 57:36 Hiring Change 59:36 Should You Study Computer Science? 01:06:10 The Bull Case and the Risks 01:13:44 Why Ramp

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Siddharth Nanda - Microsoft Engineer Reveals how Engineering Will Never be the Same Again

"Software has been solved." Most of the best engineers I know have landed on this exact conclusion. Last week I sat down with Siddharth Nanda, who went from writing every line of code by hand at Microsoft and Atlassian to shipping 30,000 lines a week with 0% of it written by him. He's now at Finch, a startup that's raised $20M+ to automate admin work at personal injury law firms, where 100% of the code he writes comes from agents. He's seen big tech before AI and a startup fully running on it, so he has a pretty unique read on where this all goes. We get into why he hasn't written a line of code in 8 months, why middle management is getting hit hardest by the layoffs, and why the productivity studies saying companies are getting slower are right, but only for companies with the wrong people. We also dig into the tooling itself: Codex vs Claude Code, what harness engineering actually is, running Devin agents in parallel, and why the cost of producing code is approaching zero, and what that means for the kind of engineer who thrives from here. If you're building with agents, this one's full of hard-won takes from someone doing it every day. Chapters 00:00 Intro 01:16 What engineering looked like at Microsoft before ChatGPT 03:08 The first AI tools inside big tech (and how gated they were) 05:17 Shipping 30,000 lines a week, none of it written by hand 06:08 Why teams are flattening 08:26 Why managers need to get back to writing code 11:51 How the management layer actually changes 14:07 What happens to PMs and designers 16:38 Why everyone becomes a builder now 20:07 How to actually learn agentic engineering 22:42 Finding new tools on X before anyone else 23:54 AI adoption is showing up in performance reviews 25:45 Building a culture that shares tools daily 27:40 The AI productivity paradox, explained 29:35 Interviewing with agents instead of against them 31:00 Why LeetCode is dead and fundamentals aren't 34:11 Who wins and who loses from here 35:36 Codex vs Claude Code: what he actually uses 36:06 What harness engineering really means 38:21 Evals and benchmarks that matter 39:59 Desktop apps, Devin, and running agents in parallel 43:30 Remote environments and working in monorepos 44:34 Skills, plugins, and hooks 47:57 Parallel worktrees and staying focused 50:42 Agent teams vs subagents 53:06 The Finch mission and wrap up

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Voice AI - The Next Frontier | Decagon, Retell, Vapi, Smallest AI, Daily

Voice agents are one of the hottest use cases in enterprise right now, but also one of the hardest to actually take live. Getting latency low enough to feel human without dumbing down the responses, making reliable tool calls to a CRM without dropping the customer mid-call, building fallback models for when Anthropic or OpenAI are running hot. None of it is as simple as the demos make it look. Last week I hosted a fireside chat with five eng leaders who deal with this stuff every day: Basia Sudol (Head of Enterprise Solutions, Decagon), Varun Singh (CPTO, Daily), Steven Diaz (FDE Manager, Vapi), Tyler D'Silva (Founding FDE, Retell AI), and Sudarshan Kamath (Founder, Smallest AI). We get into why nobody serious is shipping real-time voice-to-voice yet, why LLMs forget the middle of your prompt (and what that does to your architecture), why a giant prompt quietly destroys your unit economics, and why voice agent costs are now being compared directly against human labor. Plus the stuff nobody warns you about: turn-taking, HIPAA constraints, why outbound is easier than inbound, why getting an exec to actually like the voice can be harder than any model problem, and more! Chapters below: 00:00 Intro 00:26 Meet the panel 01:23 Daily, WebRTC, and 20 years of building voice 03:49 How Smallest AI made real-time TTS work 05:23 Why Decagon moved into voice 08:16 How Vapi and Retell think about the stack 11:14 Forward deployed vs solutions engineering 16:48 Voice agent architecture, explained simply 22:11 Cascade vs speech-to-speech: the real tradeoff 28:11 Hybrid pipelines and mixing models 32:16 Accents, multilingual, and getting Singlish right 35:32 Prompts vs workflows, and the latency fight 44:26 How you actually evaluate a voice agent 49:04 Simulation-based evals 49:49 What production metrics really look like 51:53 Building a QA framework that scales 54:53 Evaluating speech-to-speech 58:10 Open source benchmarks 59:57 Why picking a voice is so subjective 01:02:03 Personalization and custom voices 01:03:20 Voice quality is solved, GPU efficiency is the new war 01:06:23 Why outbound calls work better than you'd think 01:10:38 Deploying in regulated industries (HIPAA, retention, audits) 01:12:43 Turn-taking, the hardest unsolved problem in voice 01:18:53 Where voice agents go in the next year 01:27:55 Audience Q&A: inside Smallest's Hydra model 01:32:15 The deployment problems nobody has solved yet 01:37:46 Closing thoughts and thanks

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AI Agents in the Enterprise | Sierra, Mercor, Intercom, Turing | $2.8B+ Raised

(We know the audio quality isn't great on this one :( But the conversation is still well worth it!) Last week I hosted a fireside chat on what it actually takes to build AI agents in the enterprise with Natalie Meurer (Head of Agent Eng, Sierra), Harsh Trivedi (founding engineer, Mercor), Juhi Parekh (GM, Turing), and Kevin Lynch (Senior FDE, Fin). We get into why new models aren't always better (and why you can't just swap in the latest release and assume your agent improves), how the data-labeling/RL environment business might only have a couple years left, why real-time voice-to-voice models still aren't production-ready, how cheaper inference is still causing prices to go up, how baking in a constellation of models into enterprise agents is so important for reliability, and much, much, more! Chapters below 00:00 Intro 00:21 Meet the panel 01:57 What everyone's actually using agents for day to day 06:10 The reality of forward deployed work 09:28 What agents couldn't do a year ago that they can now 12:29 Why you have to tell agents what NOT to do 16:26 What a harness actually is 22:03 RL environments explained 28:46 Does the data-labeling and RL environment business even last? 37:02 Why benchmarks don't tell you what works in production 38:12 Agent engineering vs forward deployed engineering 41:38 Deploying into 100-year-old enterprise systems 44:25 Why AI adoption is an org problem, not a tech problem 45:36 Hiring for judgment when engineers aren't really coding anymore 48:16 Why agents are a new kind of software 50:46 The first 90 days of an enterprise deployment 53:20 Why compliance environments break normal testing 56:58 Layering AI on AI to get to 99% accuracy 01:00:56 New models aren't always better — the swap problem 01:02:35 Improving agents without waiting for a new model 01:06:36 Does agent performance secretly degrade over time? 01:09:40 Why one model is never enough: the constellation approach 01:11:14 Building resilience when inference providers go down 01:13:48 When fine-tuning actually makes sense 01:14:53 Why voice-to-voice still isn't production-ready 01:16:25 The cascaded pipeline that real voice agents use 01:21:45 Audience Q&A: managing change inside the enterprise 01:23:24 Why inference getting cheaper makes things more expensive 01:26:54 Charging for outcomes instead of conversations 01:30:19 What the real moat is when everyone uses the same models 01:37:04 Synthetic data and where the data wall actually is 01:38:50 Closing thoughts

Vince Signori: Inside LangChain's Growth Strategy from $200M to $1.25B

Today's episode is with Vince Signori, Sales Director at LangChain and one of the first sales hires at HashiCorp, where he watched the company grow from a small startup all the way to an IPO and get acquired by IBM. He sees the exact same shift happening now with AI agents that happened with cloud back then, except 50x faster. And he's got the numbers to back it up — LangChain is downloaded more than the OpenAI SDK, and 45% of the Fortune 500 are now paying customers. We get into how companies like Toyota and Home Depot are actually using AI agents in production today, why enterprises are building their own private versions of ChatGPT to own their data, and why memory is becoming the most valuable asset in AI. We also talk about what it actually takes to get an agent from prototype to production, why selling open source is the hardest sale in software, and how Vince went from 3 reps doing 20-hour days to running the number one sales region at one of the fastest growing companies in AI. You don't wanna miss this one. Chapters: 00:00 Intro 01:38 From HashiCorp to LangChain 03:20 Cloud wave vs AI agent wave 05:30 Open source vs enterprise 06:23 How LangChain's product stack evolved 07:37 Why agents are finally in production 08:57 What companies build with LangGraph 10:12 LangSmith and Engine explained 13:23 The Agent Development Lifecycle 16:36 Build vs buy on voice agents 19:48 Why owning your data and memory layer matters 22:12 How open source users become paying customers 26:49 Why LangChain hired forward deployed engineers 31:34 What go-to-market looked like with 3 reps 35:31 From 39 employees to hypergrowth 36:37 Transitioning away from founder-led sales 37:07 Why the CEO joined every early call 37:38 The sandwich sale strategy explained 39:28 Signals that an open source user is ready to buy 41:08 Why outbound controls the narrative in enterprise 43:33 Why in-person selling still wins 45:57 Building an internal GTM agent to scale 47:37 What the GTM agent actually does 50:16 Why AI moves 50x faster than cloud did 53:46 The vendor consolidation wave that's coming 56:14 How to win the platform standardization deal 59:41 Why staying model-agnostic beats the hyperscalers 01:01:23 How Vince onboards new reps today 01:04:19 The sales and engineering feedback loop 01:09:49 Signals an account is ready to expand 01:12:57 How to prove early value before full commitment 01:14:40 How enterprises actually measure agent ROI 01:16:27 Why automation is expanding beyond support 01:17:59 Which industries are adopting agents fastest 01:19:07 Healthcare agent use cases live today 01:21:17 AI agents in finance and payments 01:22:40 The Visa partnership 01:25:17 What it takes to scale a sales team right now 01:27:44 How the GTM agent is changing the SDR role 01:32:06 Why the human element in sales still matters 01:34:28 Platform deals vs point solutions 01:36:43 Vince's predictions on memory and consolidation 01:39:23 How Engine helps teams iterate on agents faster 01:43:19 Forward deployed engineers vs Engine 01:46:20 Where to find Vince and LangChain's open roles

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Shrivu Shankar - How a $5B Cybersecurity Company Runs on AI Agents

Today's episode is with Shrivu Shankar, VP of AI Strategy at Abnormal AI - a $5B cybersecurity company. What makes this one unique is that Shrivu joined as an intern in 2021 and got promoted every single year until he reached VP, so he's basically watched AI go from a niche engineering tool to something that's reshaping entire companies from the inside. We get into how AI is catching cyberattacks so sophisticated that even humans can't tell they're fake, how engineers at a $5B company have basically stopped writing code themselves, and what that means for everyone else on the team. We also go deep on why context engineering is replacing prompt engineering as the real moat, how they used GPT-3 with zero safety guardrails to generate fake phishing attacks as training data, and what it actually takes to become an AI native company at 1,500 people. One of the most technical and eye-opening conversations I've had. You don't wanna miss this one. Chapters: 00:00 Intro 00:58 Who is Shrivu and what is Abnormal AI 01:39 Why cybersecurity and machine learning 03:13 Intern to VP in 4 years — how it actually happened 05:44 What Abnormal AI does and how it started 09:10 The vendor fraud attack so convincing the victim didn't believe it was real 10:45 What GPT-3 changed for cybersecurity 13:01 Using synthetic data to train models — and how they measured it 16:49 How a 1,500 person company actually adopts AI internally 19:50 How engineering, PM, and platform roles are changing right now 23:17 The biggest AI misconception Shrivu keeps hearing 27:35 What Shrivu's day actually looks like as VP of AI Strategy 28:53 Engineers stopped writing code. Here's what they do instead. 32:28 Why product teams are getting much smaller 34:31 Why context engineering beats prompt engineering 36:31 Spec-driven development and how Nora Tech Plan works 39:14 How to scale context engineering across an entire eng org 40:30 What the manager role looks like in the agent era 42:17 What skills actually matter for managers now 43:29 AI is making orgs flatter. Is that a good thing? 45:08 How the C-suite is getting closer to the work 46:41 What agents actually are and how tool calling works 48:05 How agents improved Abnormal's detection pipeline 50:56 The AI phishing coach — how it works and why it matters 53:30 The internal AI data analyst agent 56:13 Dozens of internal agents — the ones Shrivu is most proud of 57:15 Where agents fail (it's usually not the model) 58:52 What Shrivu would tell a CEO just starting with agents 01:00:19 Sending sensitive security data to LLMs — how they handle it 01:01:47 What becoming AI native actually means in practice 01:03:37 What most people still get wrong about AI in the enterprise 01:04:31 How to write documents with AI without it sounding like AI 01:06:40 Claude Code vs Codex — which one and why 01:09:27 How Shrivu stays ahead and his take on MCPs 01:11:33 How the team uses Claude Code skills 01:12:47 Using hooks for shift-left validation in large codebases 01:13:42 How to manage context in a massive monorepo 01:14:56 Building tool-agnostic rules across Claude, Cursor, and Code Rabbit 01:16:55 Why infra teams are becoming agent harness teams 01:17:57 Wrap up

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Supriya Gupta - Meta Exec Explains How AI is Reshaping Advertising

Today's episode is with Supriya Gupta, ex-VP of Product at Intuit Credit Karma and former Product Lead on the Ads team at Meta when that business was scaling like crazy. She brings a really unique perspective on everything happening with GenAI right now because she's seen it from the inside at two of the biggest companies in tech. We get into how ads are slowly going to be generated on the spot, per user, where the image, copy and offer will all be unique to you specifically. We talk about why you can't just infinitely scale ad testing even though it's now theoretically possible, and why flooding the internet with AI content might actually be the worst thing you can do for your brand. We also go deep on what actually happened inside Credit Karma when they started building with GenAI, including what moved the needle and what didn't, and what that means for designers, PMs, and content teams everywhere. And at the end, Supriya shares why she walked away from her VP role to start her own company, and what she's building now. You don't wanna miss this one. 🔗 Find Supriya on LinkedIn: https://www.linkedin.com/in/supriyag/ 🌐 Company's website: https://www.helloeve.co/ ⏱ Chapters 00:00 Intro 01:27 Predictive AI vs. Generative AI: what actually changed 02:59 The next gen of dynamic ads — why every user could soon get a unique ad 06:52 Will content agencies survive the AI era? 09:17 Why you can't just test a million ad variants (the stats problem) 12:55 AI slop, UGC backlash, and should AI content be labeled? 17:02 Supriya joins Credit Karma: building the Lightbox targeting platform 21:23 Building the Credit Karma financial assistant 24:35 Handling hallucinations in a finance app at scale 27:26 What happened to content designers when AI started writing copy 29:56 Why AI copy still needs human taste and judgment 31:01 PMs are prototyping now — what that means for design and eng 33:30 Will there be fewer PMs? (Probably not — here's why) 35:33 Why Supriya left Credit Karma to start her own company 37:23 The principles she built her startup around 39:20 The rise of the "super IC" — managers becoming AI-powered operators 41:54 Why most AI projects fail before they even start 45:33 Enterprises vs. startups: how they approach AI differently 48:13 What a successful AI deployment actually looks like 51:02 Will everyone need to upskill on AI? (Spoiler: maybe not) 52:41 Most execs are thinking about cost-cutting — the smarter ones aren't 53:49 The executive digital twin: Supriya's startup vision 56:51 Current product, roadmap, and what's shipping next 58:52 Where to find Supriya

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The Future of Agentic Engineering | Cognition (Devin), Semgrep, Factory & Composio | $1.2B+ Raised

AI agents are everywhere right now. But are they actually working inside real engineering teams? At AngelList’s Founders Cafe, I sat down with founders of Cognition ($898M raised), Semgrep ($204M raised), Factory ($70M raised), and Composio ($29M raised) to talk about what agentic engineering looks like in practice. There’s a lot of hype around AI coding tools, but the reality is more nuanced. Some teams are moving 10x faster, others are slowing down. A big part of it comes down to whether your codebase is actually “agent-ready” (linting, type systems, guardrails, etc). We also went deep on security, which is one of the biggest gaps right now. As more non-developers start “vibe coding,” the risk surface grows fast. We talked about MCP access control, layered security, and why you can’t rely on models alone to generate secure code. Enterprise teams are also dealing with the operational side of this shift. They have to manage cost, run evals, and help thousands of engineers use these systems well. Tools like PR review agents, model routing, and internal orchestration are quickly becoming part of the stack. ⏱ Chapters 00:00 Welcome and setup 00:41 Panel introductions 02:00 Windsurf acquisition 03:52 Do agents boost productivity? 04:16 Agent-ready codebases 05:55 Real-world enterprise wins 07:28 Agents building integrations 09:41 Security risks (vibe coding) 11:04 LLM security tools landscape 12:21 Defense-in-depth 15:16 MCP security pitfalls 18:41 MCP vs CLI 23:08 RL for secure code 27:05 Auto research missions 27:57 Training your own models 31:33 Distillation and IP decay 34:50 Hybrid systems 37:14 Side projects vs enterprise 40:16 Forward deployed engineering 40:58 Agent orchestration 43:08 Cost controls 43:49 Auto model routing 45:52 Guardrails 47:02 Legacy code risks 48:16 Model poisoning 49:35 What is a harness? 51:47 Why build your own 52:58 Continuous learning loops 56:30 Security workflows 57:37 Validation and meta engineering 1:00:32 Running evals in practice 1:03:36 Teams reshaped by agents 1:12:17 Should you study CS? 1:14:11 Enterprise adoption 1:18:23 Local to cloud journey 1:20:51 Agent economy and prompting 1:22:22 Spec vs plan 1:25:12 Closing and thanks

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He Built a $200M AI Agent 10 Years Before ChatGPT

Summary: In this conversation, I talked to Ashish Shubham (VP of Engineering), who's been at ThoughtSpot for 10 years, about AI agents in enterprise analytics. ThoughtSpot started as a search-based analytics company trying to make data accessible to regular business users. In 2019, they tried building natural language interfaces using BERT, but only hit about 50% accuracy. For a product where enterprise customers make billion-dollar decisions, that wasn't good enough. They shelved the project. When ChatGPT came out, ThoughtSpot was ready. Ashish walked me through how they pivoted: they built a 25-30 person team, decided to use prompting instead of fine-tuning, and leveraged their existing semantic data modeling layer to get accuracy into the high 90s. We got into the technical evolution from monolithic systems to agent architectures with tools, how they went from manual human judges to using LLMs to evaluate their outputs, and how enterprise security requirements shaped what they built. We also talked about how software engineering is changing. Ashish said 50-60% of his code is AI-generated now, and he thinks system design is becoming the critical skill, even for junior engineers. He had an interesting take on the "95% of AI deployments fail" stat too. Chapters: 0:00 Intro and Ashish's journey to ThoughtSpot from GoDaddy 0:13 ThoughtSpot's mission to democratize data analytics for business users 1:26 Early search-based analytics before natural language processing 2:36 ThoughtSpot vs Tableau and the promise of self-service analytics 4:40 The analyst bottleneck problem and how ThoughtSpot aimed to solve it 5:49 Early technical challenges with in-memory databases and data migration 8:11 Semantic data models, joins, and creating abstraction layers for users 11:39 Who builds the data models and the role of analysts 12:22 Pre-LLM natural language processing using BERT and word2vec in 2018-2019 14:43 The accuracy problem and ambiguity in translating user queries 16:58 Trust challenges and why the early NLP product never became core 19:59 Competition with Tableau, Looker, and Power BI 22:44 How analyst roles changed with self-service analytics tools 25:30 The ChatGPT moment and pivoting to LLM-powered natural language 27:48 Early prompt engineering days and generating SQL with LLMs 31:09 Training vs prompting debate and why fine-tuning was eventually abandoned 34:28 Organizational changes and building the NLS team 37:16 Coaching systems for company-specific terminology vs training models 39:02 Evolution of evaluation methods from human judges to LLM-as-judge 43:23 Moving to LangFuse and GCP for agent infrastructure 46:29 How LLM context windows and capabilities evolved their product 50:07 From 30-column limits to agentic systems with 90%+ accuracy 52:52 RAG, column selection, and using proprietary data indexes 54:59 Multi-model support and enterprise data security concerns 59:14 How AI has changed Ashish's personal engineering workflow 1:02:42 Impact of AI on the broader engineering organization 1:04:15 Measuring AI productivity and the challenge of metrics 1:07:26 50-60% AI-generated code and the changing nature of coding 1:09:18 System design skills becoming more important than coding 1:13:00 Junior engineers doing senior-level work and interview changes 1:14:37 Customer conversations about Gen AI adoption across industries 1:17:26 The MIT report on 95% agent failures and why it misses the point 1:22:12 Agent architecture with LangGraph vs Google ADK and building internal agent platform 1:24:26 Where value lies in the next two years: tools, skills, and optimization 1:28:05 Startup opportunities in making AI accessible to non-technical users 1:29:26 Closing remarks

He Led AI Transformation for Angry Birds. Then He Quit.

In this conversation, Tatu discusses the transformative impact of AI on game development, drawing from his extensive experience in the gaming industry. He highlights the shift from traditional game development processes to a more agile, AI-driven approach that allows for rapid prototyping and iteration. Tatu emphasizes the importance of organizational change and the need for leaders to embrace AI as a core part of their strategy. He also explores the evolving role of product managers, the challenges of user acquisition, and the future of marketing in a saturated gaming market. The discussion culminates in Tatu's vision for his new AI-native game studio, aiming to disrupt the industry by leveraging cutting-edge technology to create high-quality games at unprecedented speed. Takeaways: AI is condensing the time and resources needed for game development. Organizational inertia can hinder the adoption of AI in large companies. The future of game development will require T-shaped professionals with diverse skills. AI will fundamentally change the economics of the gaming industry. Smaller companies can leverage AI to outmaneuver larger competitors. The role of product managers will evolve as AI takes over prioritization tasks. Marketing strategies will need to adapt to a more saturated market. User acquisition costs are expected to rise due to increased competition. Novelty may not be as valuable as familiarity in a saturated market. The future of entertainment will see a rise in fast, iterative game development. Chapters: 00:00 The Evolution of Game Development with AI 03:07 From Web Design to Gaming: A Career Journey 05:50 The Impact of AI on Knowledge Work 09:07 The Changing Landscape of Game Development 11:53 Organizational Inertia and the Future of Gaming Companies 14:55 The Role of AI in Transforming Game Development 17:57 Navigating the Challenges of AI Adoption 21:08 The Future of Game Development Methodologies 23:46 The Role of Product Managers in an AI-Driven World 26:47 Marketing Strategies in the Gaming Industry 29:59 The Role of Publishers in Game Development 33:05 The Future of User Acquisition in Gaming 36:02 The Changing Economics of Game Development 38:56 The Future of Software Development 42:13 The Role of Novelty in Game Development 45:04 The Importance of Familiarity in a Saturated Market 48:12 The Future of Fast Entertainment 50:59 Leveraging Licensing for Success 54:02 The Journey from Rovio to AI Native Gaming 57:02 Building Tools for Rapid Game Development 59:57 The Vision for Future Games 01:03:04 AI Adoption in Organizations: A Leader's Perspective