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Datadog CISO Emilio Escobar on Securing Agents at Scale In this conversation, Emilio Escobar joins a16z's Joel de la Garza at Black Hat to cover what happens when you hand coding agents to 4,000 engineers, why the permissioning that worked for a decade broke the moment agents could write... show more

a16z

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47,472 views • 12 days ago •via X (Twitter)

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Datadog CISO Emilio Escobar says intent is now a must-have for AI security solutions: "These agents are trained on existing code, and they have a reward structure. If code is meant to solve the bug, but it gets rewarded on that, it doesn't care if it's doing something else outside of that." "You have to be careful how you prompt these things, but also how it actually interprets your prompt and executes on that. So we have this judge now evaluating the code output of the agents to then make sure that we're doing that." "I did a roundtable last week about agentic security, and the sense that I got from a bunch of the security leaders was a sense of helplessness. Of just waiting for a commercial solution to come in and solve it all." Emilio Escobar Datadog, Inc.

Datadog CISO Emilio Escobar says intent is now a must-have for AI security solutions: "These agents are trained on existing code, and they have a reward structure. If code is meant to solve the bug, but it gets rewarded on that, it doesn't care if it's doing something else outside of that." "You have to be careful how you prompt these things, but also how it actually interprets your prompt and executes on that. So we have this judge now evaluating the code output of the agents to then make sure that we're doing that." "I did a roundtable last week about agentic security, and the sense that I got from a bunch of the security leaders was a sense of helplessness. Of just waiting for a commercial solution to come in and solve it all." Emilio Escobar Datadog, Inc.

a16z

34,697 views • 12 days ago

"We are clearly entering a world where a product-minded engineer is now empowered to produce software without writing a line of code for it." In this conversation, Temporal CEO Samar Abbas joins a16z GPs Sarah Wang and Raghu Raghuram to cover: - Why agents are going from short-lived and interactive to long-running and async - Why the engineer of the future manages 15 parallel AI tasks at once - How OpenAI Codex runs millions of concurrent agent executions on Temporal - How real-time context engineering is exploding on Temporal - Why SaaS isn't dead, and value is migrating to APIs 00:00 Introduction 04:03 Temporal's origin story 11:14 Why agents raise the stakes 16:00 Specialized agents need durable RPC 25:20 Deep research agents 30:58 Execution histories as a superpower 39:04 Minimal viable long-running agent architecture 45:07 Context engineering at scale 52:40 Where value accrues: The "five-layer cake" and breakout AI applications Samar Abbas Sarah Wang Raghu Raghuram

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a16z

45,989 views • 6 months ago

FULL INTERVIEW: Sam Altman joins TBPN to discuss GPT-5.3-Codex, AI agents, Anthropic's Super Bowl ads, and more. 00:00 GPT-5.3-Codex 02:27 AI agents and the future of work 03:20 The role of forward-deployed engineers in AI 05:42 AI benchmarks 07:29 Emotional attachment to chatbots 10:40 On data and compute being the 'new oil' 12:56 Is software dead? 17:48 Codex Desktop and the rise of the general-purpose work agent 25:00 OpenAI’s last Super Bowl ad and the Anthropic ads

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TBPN

639,180 views • 6 months ago

“Coding will eat all knowledge work” Peter Yang joins a16z’s Anish Acharya to discuss the post-AI future of work, why AI will create more solopreneurs, why human ambition means there will always be new jobs, and more. 00:00 Intro 01:56 Using OpenClaw for voice, memory & daily life 06:14 Will agents kill apps & SaaS? 11:57 Coding agents: Claude Code vs. Codex 17:00 Future of work: small teams, agents & company culture 24:00 How agents change consumer products & the economy Peter Yang Anish Acharya

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a16z

96,476 views • 4 months ago

Benedict Evans on Why AI Feels Like the Internet in 1997 Benedict Evans joins Erik Torenberg for a conversation on the state of AI, including how coding agents hit product-market fit, why foundation models should be thought of as infrastructure, the value of vertical products, and more. 00:00 Intro 00:44 What's changed since last year 05:53 OpenAI vs Anthropic strategy 10:31 The pricing crunch & platform history 22:48 What comes after coding 38:18 AI & the future of enterprise software 48:43 The CapEx problem 55:07 Will models become commodities? Benedict Evans Erik Torenberg

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a16z

103,588 views • 2 months ago

Jesse Genet on Agentic Parenting Jesse Genet joins a16z's Sarah Wang and Katherine Boyle to discuss her journey from founder to parent, how she's using agents in her household, and how AI could transform parenting for the better. 00:00 YC founder turned homeschool mom 03:00 Discovering Claude Code and agentic building 06:00 Building while homeschooling 4 kids under 5 11:00 How AI generates personalized lesson plans and logs progress 18:00 Jesse's 11-agents 27:05 Agent tech stack deep dive 33:56 How agents improve daily life 40:04 Letting kids interact with AI: values, risks, and the future of parenting Jesse Genet Katherine Boyle Sarah Wang

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a16z

1,791,972 views • 4 months ago

Box CEO Aaron Levie on the AI Adoption Gap Aaron Levie joins Steven Sinofsky, Martin Casado, and Erik Torenberg to discuss how AI agents will revolutionize work, the growing pains of building software for the agent economy, what Wall Street gets wrong about AI, and more. 00:00 Intro 00:51 Building software for agents vs. humans 02:10 Can non-technical workers actually use AI agents? 14:31 CFO/CIO pushback: the real fear of agents doing integration 18:39 Treating agents like employees and why it breaks down 27:35 Diffusion gap: startups vs. enterprises 42:53 What Wall Street gets wrong Aaron Levie Steven Sinofsky martin_casado Erik Torenberg

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a16z

276,989 views • 4 months ago

Raising An Agent is back! In the first episode of season 2, Quinn Slack and Thorsten Ball discuss why the Amp team went heads-down the last few months to rebuild Amp around remote-controllable, parallel agents, running locally or in orbs. They also talk about how this shift changed our development workflows and what that means for the future of local development. 00:00 Intro: Is local development obsolete? 00:53 What we've been up to since "the coding agent is dead" 03:03 The Amp Neo rewrite and what has changed 07:38 From unconstrained tokens to unconstrained parallel agents 11:25 How orbs change our development workflow 18:25 Cloud agents are underestimated 20:18 Killing obsolete features & choosing agents as a team 23:35 Why developers overestimate local development 27:54 Pushing straight to main & rethinking CI 31:20 How agents will reshape teams, products & companies 36:40 Complex dev environments & standardization 43:20 Amp subscriptions 45:55 What’s shipping next

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Amp

76,780 views • 1 month ago

GitButler is redesigning the version control experience for you and your agents. We're very excited to lead their Series A. In this conversation, GitButler CEO Scott Chacon joins a16z GP Matt Bornstein to discuss: - Why Scott came back to version control after GitHub - Why Git's UI has barely changed since 2005 - How GitButler is building version control designed natively for agentic coding - Why AI makes communication a more valuable skill for developers 00:00 Intro 01:11 Why Scott came back to version control after GitHub 06:18 How Git was actually built 11:32 Designing GitButler's CLI for agents 18:05 Parallel branches: How GitButler handles multi-agent workflows 23:33 What happens to GitHub in an agentic world 27:00 Code review needs a rethink: PRs, commit messages, and what comes next 32:19 Writing and communication as the new developer superpower Scott Chacon Matt Bornstein

GitButler is redesigning the version control experience for you and your agents. We're very excited to lead their Series A. In this conversation, GitButler CEO Scott Chacon joins a16z GP Matt Bornstein to discuss: - Why Scott came back to version control after GitHub - Why Git's UI has barely changed since 2005 - How GitButler is building version control designed natively for agentic coding - Why AI makes communication a more valuable skill for developers 00:00 Intro 01:11 Why Scott came back to version control after GitHub 06:18 How Git was actually built 11:32 Designing GitButler's CLI for agents 18:05 Parallel branches: How GitButler handles multi-agent workflows 23:33 What happens to GitHub in an agentic world 27:00 Code review needs a rethink: PRs, commit messages, and what comes next 32:19 Writing and communication as the new developer superpower Scott Chacon Matt Bornstein

a16z

29,353 views • 4 months ago

"The issue is it wasn't built for humans." Yat Siu (Yat Siu) co-founded Animoca Brands. Started building NFT infrastructure when CryptoKitties melted Ethereum. Made the AI + crypto argument years before anyone listened. The man who has spent a decade selling crypto to retail now thinks the next customer won't be retail at all. "There's going to be more agents than humans. The entire internet is going to be swarmed by agents." We cover: Why AI agents, not humans, become the actual customer crypto was built for The infrastructure that has to exist before agents can transact at machine speed Why every consumer app breaks when agents replace browsing (the Tinder example) How meme coins pulled focus from the builders who actually mattered Why tokens become the commodity layer for compute, energy, and attention The orchestration layer thesis, where real development is happening now Why the metaverse isn't dead, it's just coming to us through agents What retail should actually be holding right now Thanks to Yat for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 02:00 - Crypto Is Trump's 10th Child 07:30 - Meme Coins Were The Wrong Distraction 12:00 - The Customer Was Never You 17:30 - Code Is Law For Agents 22:00 - More Agents Than Humans 27:00 - The End Of Websites 32:00 - Why On-Chain Growth Stalled 36:00 - What Retail Should Actually Hold

"The issue is it wasn't built for humans." Yat Siu (Yat Siu) co-founded Animoca Brands. Started building NFT infrastructure when CryptoKitties melted Ethereum. Made the AI + crypto argument years before anyone listened. The man who has spent a decade selling crypto to retail now thinks the next customer won't be retail at all. "There's going to be more agents than humans. The entire internet is going to be swarmed by agents." We cover: Why AI agents, not humans, become the actual customer crypto was built for The infrastructure that has to exist before agents can transact at machine speed Why every consumer app breaks when agents replace browsing (the Tinder example) How meme coins pulled focus from the builders who actually mattered Why tokens become the commodity layer for compute, energy, and attention The orchestration layer thesis, where real development is happening now Why the metaverse isn't dead, it's just coming to us through agents What retail should actually be holding right now Thanks to Yat for coming on New Era Finance Podcast. TIMESTAMPS: 00:00 - Intro 02:00 - Crypto Is Trump's 10th Child 07:30 - Meme Coins Were The Wrong Distraction 12:00 - The Customer Was Never You 17:30 - Code Is Law For Agents 22:00 - More Agents Than Humans 27:00 - The End Of Websites 32:00 - Why On-Chain Growth Stalled 36:00 - What Retail Should Actually Hold

Michaël van de Poppe

122,039 views • 2 months ago

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

Everything Gets Rebuilt: my conversation with Harrison Chase, CEO of LangChain about agent harnesses, evals, runtimes, sandboxes, MCP and the future of the agent stack 00:00 Intro - meet Harrison Chase - at the Chase Center for the Daytona Compute conference 01:32 What changed in agents over the last year 03:57 Why coding agents are ahead 06:26 Do models commoditize the framework layer? 08:27 Harnesses, in plain English 10:11 Why system prompts matter so much 13:11 The upside — and downside — of subagents 15:31 Why a useful agent needs a filesystem 18:13 Additional core primitives of modern agents 19:12 Skills: the new primitive 20:19 What context compaction actually means 23:02 How memory works in agents 25:16 One mega-agent or many specialized agents? 27:46 The future of MCP 29:38 Why agents need sandboxes 32:35 How sandboxes help with security 33:32 How Harrison Chase started LangChain 37:24 LangChain vs LangGraph vs Deep Agents 40:17 Why observability matters more for agents 41:48 Evals, no-code, and continuous improvement 44:41 What LangChain is building next 45:29 Where the real moat in AI lives

Matt Turck

35,253 views • 5 months ago

Amjad Masad and Marc Andreessen on AI agents, AGI, Creativity, and Reasoning Agents are getting better, fast. Replit Agent v1 could do only 2 minutes of coherent work. v2 could do 20 minutes. Today’s v3? 200 minutes. As agents get more and more reliable, we can make huge progress in coding without ever achieving “true” AGI. In this episode, Marc Andreessen and Erik Torenberg sit down with Replit ⠕ co-founder and CEO Amjad Masad to discuss the upward trajectory of agents, how RL unlocked reasoning for modern models, why verification loops changed everything, and whether LLMs are hitting diminishing returns. 00:00 Intro 03:00 The vision behind Replit 07:00 Building apps with AI agents 09:30 When the agent becomes the programmer 13:45 Reinforcement learning and problem solving 17:30 The verification loop and multi-agent systems 26:00 Why coding is advancing faster than other Fields 33:45 AGI debate 41:15 Functional AGI and automating labor 53:10 Creativity, reasoning, and finding truth in AI 57:30 The origins of Replit 01:03:00 Hacking his university and getting caught Amjad Masad Marc Andreessen 🇺🇸 Erik Torenberg

Amjad Masad and Marc Andreessen on AI agents, AGI, Creativity, and Reasoning Agents are getting better, fast. Replit Agent v1 could do only 2 minutes of coherent work. v2 could do 20 minutes. Today’s v3? 200 minutes. As agents get more and more reliable, we can make huge progress in coding without ever achieving “true” AGI. In this episode, Marc Andreessen and Erik Torenberg sit down with Replit ⠕ co-founder and CEO Amjad Masad to discuss the upward trajectory of agents, how RL unlocked reasoning for modern models, why verification loops changed everything, and whether LLMs are hitting diminishing returns. 00:00 Intro 03:00 The vision behind Replit 07:00 Building apps with AI agents 09:30 When the agent becomes the programmer 13:45 Reinforcement learning and problem solving 17:30 The verification loop and multi-agent systems 26:00 Why coding is advancing faster than other Fields 33:45 AGI debate 41:15 Functional AGI and automating labor 53:10 Creativity, reasoning, and finding truth in AI 57:30 The origins of Replit 01:03:00 Hacking his university and getting caught Amjad Masad Marc Andreessen 🇺🇸 Erik Torenberg

a16z

741,248 views • 10 months ago

Trillions in AI compute can't buy a $10 domain name. Jeff Weinstein explains how Stripe’s new machine payments protocol will unleash autonomous agents to control real budgets. (0:00) What Is Agentic Commerce? (2:00) What Is the Machine Payments Protocol? (4:11) Why Can’t Smart Agents Buy Things Online? (7:14) How Can You Safely Give an Agent a Budget? (9:34) How Can Agents Provision Cloud Services? (12:22) Why Does Machine Payments Need Stablecoins? (14:15) Why Did Stripe Co-Found the Tempo Blockchain? (16:52) Who Are the Early Adopters of Agent Payments? (18:38) Why Are Agents Now Stripe’s Primary Customer? (20:00) How Did Stripe Atlas Reach 100,000 Startups? (23:09) Why Are AI Startups Growing 50% Faster Now? (24:59) Who Is Smarter: Patrick or John Collison?

Trillions in AI compute can't buy a $10 domain name. Jeff Weinstein explains how Stripe’s new machine payments protocol will unleash autonomous agents to control real budgets. (0:00) What Is Agentic Commerce? (2:00) What Is the Machine Payments Protocol? (4:11) Why Can’t Smart Agents Buy Things Online? (7:14) How Can You Safely Give an Agent a Budget? (9:34) How Can Agents Provision Cloud Services? (12:22) Why Does Machine Payments Need Stablecoins? (14:15) Why Did Stripe Co-Found the Tempo Blockchain? (16:52) Who Are the Early Adopters of Agent Payments? (18:38) Why Are Agents Now Stripe’s Primary Customer? (20:00) How Did Stripe Atlas Reach 100,000 Startups? (23:09) Why Are AI Startups Growing 50% Faster Now? (24:59) Who Is Smarter: Patrick or John Collison?

Rex Salisbury

21,091 views • 2 months ago

How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence

How will companies build agents that work alongside their employees? I discuss this with Guillermo Rauch, the CEO of Vercel, who’s building V... an internal agent used by almost 1,000 people across Vercel. We talk about: - The Openclaw Movement - How to get started building agents for your team? - Do we build a team of many agents or one GOD agent? - Hard parts of creating agents - Agents that delegate to other agents with different permissions and computer access - Eve The framework for building agents This is Agent Native #4 00:00 Intro 01:21 The State of Agent Adoption 07:26 "V" - Vercel's Internal Company Agent 14:32 Lessons from OpenClaw: Soul & Computer use 22:20 One God Agent OR a Team of Agents 27:11 Build Your Own Agent: Skills, Tools & Permissions 33:52 Proactive Agents, Triggers & Self-Improvement 40:30 Who Will Build Agents & Getting Started 46:22 Open Models - Cheaper, Faster, Better 53:05 Closing Thoughts: Owning Your Own Intelligence

Riley Brown

75,929 views • 18 days ago

🤖 Bots with Wallets: What Could Possibly Go Right? In this episode of CDPod, yuga.eth 🛡 and erik.eth 🛡 are joined by EtherMage, co-founder of Virtuals Protocol. They break down the emergence of wallet-enabled AI agents, why crypto is the missing piece for real autonomy, and how tokenization creates new ways for people to benefit from agent-driven economies. Timestamps: 00:00 - Intro 01:26 - Introducing Jansen Tang from Virtuals Protocol 03:27 - From gaming DAO to Roblox autonomous NPCs 04:01 - Expanding into entertainment and TikTok agents 06:39 - Giving agents agency, goals, and memory 11:01 - Launching Luna as tokenized TikTok influencer 13:54 - Luna’s market cap rise, inspiring other agents 16:00 - Different schools of thought in AI 20:08 - How agents interact with each other 24:00 - Problems faced: information loss 25:05 - How blockchain solves information loss 29:40 - Agent economy stack and ACP’s role 30:39 - x402 enabling agent payments and wallets 32:33 - Combining tools, agents, and x402 32:59 - What agents do on ACP today 35:55 - Information agents like AIXBT 36:41 - Personal finance agents 38:08 - Current adoption 40:07 - Making agents richer 42:09 - Robotics gaps: data and real‑world testing 44:11 - Closing, ending questions

🤖 Bots with Wallets: What Could Possibly Go Right? In this episode of CDPod, yuga.eth 🛡 and erik.eth 🛡 are joined by EtherMage, co-founder of Virtuals Protocol. They break down the emergence of wallet-enabled AI agents, why crypto is the missing piece for real autonomy, and how tokenization creates new ways for people to benefit from agent-driven economies. Timestamps: 00:00 - Intro 01:26 - Introducing Jansen Tang from Virtuals Protocol 03:27 - From gaming DAO to Roblox autonomous NPCs 04:01 - Expanding into entertainment and TikTok agents 06:39 - Giving agents agency, goals, and memory 11:01 - Launching Luna as tokenized TikTok influencer 13:54 - Luna’s market cap rise, inspiring other agents 16:00 - Different schools of thought in AI 20:08 - How agents interact with each other 24:00 - Problems faced: information loss 25:05 - How blockchain solves information loss 29:40 - Agent economy stack and ACP’s role 30:39 - x402 enabling agent payments and wallets 32:33 - Combining tools, agents, and x402 32:59 - What agents do on ACP today 35:55 - Information agents like AIXBT 36:41 - Personal finance agents 38:08 - Current adoption 40:07 - Making agents richer 42:09 - Robotics gaps: data and real‑world testing 44:11 - Closing, ending questions

Coinbase Developer Platform🛡️

66,581 views • 9 months ago

You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead

You’ve probably already heard all about OpenClaw (formerly Clawdbot/Moltbot). Now meet the man behind it. YC’s Raphael Schaad sat down with Peter Steinberger 🦞, the creator of OpenClaw, to discuss the “aha” moment behind the viral personal AI agent, why local-first agents could replace many of today’s apps, and how personal agents will reshape the future of software. 00:00 – OpenClaw takes over the internet 00:44 – Life after going viral 01:28 – Why OpenClaw took off, what sets it apart 02:56 – Bots talking to bots (and hiring humans) 04:11 – From “God AI” to swarm intelligence 05:07 – Peter’s original “aha” moment 06:38 – Rebuilding the agent as a conversation 07:38 – The moment it exceeded expectations 10:21 – Are apps going to disappear? 12:31 – Memory, data silos, and ownership 14:39 – The privacy reality of personal agents 15:05 – Letting the bot loose in public Discord 16:55 – Giving an agent a personality 18:19 – Contrarian building philosophy 20:09 – CLIs vs MCPs 21:28 – Building for humans first 21:46 – The road ahead

Y Combinator

359,440 views • 6 months ago

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders

How to build long-horizon AI agents: behavior specs, ontologies, process supervision - my conversation with Mitchell Troyanovsky, co-founder of Basis 01:09 Why Everyone at Basis Was Whispering to AI when Stephanie Palazzolo walked in 04:12 Accounting as "an Intelligence Over the Economy" 06:11 What Makes an Agent Truly Long-Horizon 08:24 Inside an Autonomous, Multi-Day Tax Return 10:19 Agents That Hand Off Like Senior Engineers 11:17 A Brief History of Agents: From ReAct to Today 12:33 Why LLMs Have No Long-Term Memory 14:13 Why AutoGPT Didn't Live Up to Its Promise 15:51 The Three Breakthroughs: Opus 3, o1, o3 17:07 Why Reasoning Models Unlocked Agents 18:23 "Let's Verify Step by Step": The Road Not Taken 20:32 Pushing Back on the METR Chart 22:09 Why Coding Agents Won First 25:14 Why Real-World Agents Are Harder 26:55 How Accountants Verify Non-Deterministic Work 29:18 You Can't Scale Tax Returns Like Math 33:16 100 Evals Pass - So What? 35:53 Right Answer, Wrong Process 36:37 Behavior Specs, Explained 39:58 How Specific Should Behaviors Be? 42:18 Context Is Runtime Training Data 44:21 Who Judges the Judge? 46:45 The Move 37 Objection 50:02 The Magic Box Mental Model 52:41 "Nothing Has Changed Since o3" 54:56 Open-Sourcing Behavior Specs with Ankur Goyal Braintrust 59:45 Ontologies: A World for Agents to Live In 01:04:20 Documentation as Codebase 01:06:33 Why the Founding Fathers Were Context Engineers 01:09:05 Onboarding 300 Brilliant Alien Employees 01:11:10 Self-Improving Agent Systems 01:12:50 The Context Mistake Agent Builders Make 01:14:29 RL on Behavior Adherence 01:17:01 Will the Bitter Lesson Swallow the Harness 01:18:46 "Technical Moats Are Not Real Moats" 01:21:03 Advice for AI Builders

Matt Turck

20,898 views • 17 days ago

"Intelligence isn't the boundary anymore. Safety is what's actually constraining agents." I had a great conversation with tusharjain, Head of Engineering at Docker, at our AI Engineer booth. Tushar leads engineering at Docker, the containerization platform millions of developers use to package and ship software, and his team is now rebuilding that engine into a runtime for AI agents with safety built in. Fresh off his talk, we got into: 00:57 - Why safety, not intelligence, is the real constraint on agents 02:00 - The three pillars: contain agents, scope their tools, layer on policy 04:04 - Background agents and the "laptops half open so the agent doesn't die" problem 08:13 - Going 100% agents on Docker's own engineering team 11:28 - Agent identity: you can fire a human, but not their agent Thanks for coming to the Browserbase booth!

"Intelligence isn't the boundary anymore. Safety is what's actually constraining agents." I had a great conversation with tusharjain, Head of Engineering at Docker, at our AI Engineer booth. Tushar leads engineering at Docker, the containerization platform millions of developers use to package and ship software, and his team is now rebuilding that engine into a runtime for AI agents with safety built in. Fresh off his talk, we got into: 00:57 - Why safety, not intelligence, is the real constraint on agents 02:00 - The three pillars: contain agents, scope their tools, layer on policy 04:04 - Background agents and the "laptops half open so the agent doesn't die" problem 08:13 - Going 100% agents on Docker's own engineering team 11:28 - Agent identity: you can fire a human, but not their agent Thanks for coming to the Browserbase booth!

Shubhankar

20,947 views • 1 month ago

AI agents at Kavak sell the cars, underwrite the loans, coach the mechanics, and in one Mexican city, run the entire operation. The Latin American used-car marketplace bet on agents three years ago. Today ~95% of interactions and transactions run end-to-end on AI: NPS tripled, sales conversion doubled, warranties down 26%, car loans approved in less than three minutes. CPO & AI Officer Alejandro Maza joins a16z's Angela Strange and Gabriel Vasquez on how they pulled it off, why adoption without redesign fails, and why they deleted two years of working architecture to start over. 00:00 Intro 01:03 ML before transformers 02:23 Kavak's agent-per-customer architecture 04:59 Three bets: redesign the company, build superhuman agents, change the metrics 10:49 Agents with 2.1x conversion 14:23 Car loans approved in minutes 16:13 1.5x profits in a real city in six weeks 20:13 "Jedi Academy": training mechanics to ship agents 28:44 Destroying two years of work 32:52 Ford's factory: why adoption isn't enough 34:45 Advice for founders Alejandro Maza angela strange Gabriel Vasquez

AI agents at Kavak sell the cars, underwrite the loans, coach the mechanics, and in one Mexican city, run the entire operation. The Latin American used-car marketplace bet on agents three years ago. Today ~95% of interactions and transactions run end-to-end on AI: NPS tripled, sales conversion doubled, warranties down 26%, car loans approved in less than three minutes. CPO & AI Officer Alejandro Maza joins a16z's Angela Strange and Gabriel Vasquez on how they pulled it off, why adoption without redesign fails, and why they deleted two years of working architecture to start over. 00:00 Intro 01:03 ML before transformers 02:23 Kavak's agent-per-customer architecture 04:59 Three bets: redesign the company, build superhuman agents, change the metrics 10:49 Agents with 2.1x conversion 14:23 Car loans approved in minutes 16:13 1.5x profits in a real city in six weeks 20:13 "Jedi Academy": training mechanics to ship agents 28:44 Destroying two years of work 32:52 Ford's factory: why adoption isn't enough 34:45 Advice for founders Alejandro Maza angela strange Gabriel Vasquez

a16z

361,681 views • 14 days ago