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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 次观看 • 13 天前 •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

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a16z

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a16z

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a16z

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a16z

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a16z

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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

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Riley Brown

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🤖 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 次观看 • 9 个月前

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 次观看 • 6 个月前

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 次观看 • 18 天前

"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 次观看 • 1 个月前

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 次观看 • 14 天前