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Raindrop (Raindrop) is building the safety layer for AI agents. As agents get more capable and take on more complex work, Raindrop detects when they go wrong in production, from failed tool calls and hallucinations to problems companies didn’t even know to look for. Now they’re launching Raindrop Simulations,...

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Y Combinator

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50,858 Aufrufe • vor 1 Tag •via X (Twitter)

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9 Kommentare

Profilbild von Nicholas Charriere
Nicholas Charrierevor 1 Tag

@raindrop_ai 4 awesome people in one vid, congrats all around

Profilbild von Fireply.ai
Fireply.aivor 1 Tag

@raindrop_ai a tool built to debug their own agent turning into the company is the most honest founding story on this list

Profilbild von Chinsanity
Chinsanityvor 1 Tag

@raindrop_ai Bookmarking this

Profilbild von Samuel Hu
Samuel Huvor 1 Tag

@raindrop_ai the failed tool call case is the one i keep hitting. a coding agent can burn 20 minutes on one bad command unless the run writes the exact error before retrying.

Profilbild von Japan Startup Feed | VPO
Japan Startup Feed | VPOvor 1 Tag

@raindrop_ai Agent safety at scale is the defining infrastructure challenge now. As multi-agent systems move from workflows to autonomous execution, building real-time production guardrails is brilliant. Fascinating to watch this layer mature globally.

Profilbild von مُحَمَّدٌ الشِّهَابِيُّ | Mohammed Al-Shihabi
مُحَمَّدٌ الشِّهَابِيُّ | Mohammed Al-Shihabivor 1 Tag

@raindrop_ai Agent failures happen because teams force probabilistic LLMs to do deterministic jobs. When you replace cloud hallucinations with mathematically bound expert logic on local silicon, the "failure surface" shrinks to zero. Logic > Probabilistic bloat. ♟️

Profilbild von Jordan Van Ahn
Jordan Van Ahnvor 1 Tag

@raindrop_ai ♥️♥️♥️

Profilbild von James Camarota
James Camarotavor 1 Tag

@raindrop_ai Production detection is where this gets real. Can Raindrop trace a bad outcome back to the tool call or model decision that caused it?

Profilbild von NAMAN RAJ
NAMAN RAJvor 1 Tag

@raindrop_ai Good luck with the life preserver for AI's wild ride! 💦😬

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Lunar

351,529 Aufrufe • vor 1 Monat

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11
43:20

In the future, you’ll be able to accomplish a goal by just giving Claude an outcome and a budget. That’s the direction Anthropic is building in with its new Managed Agents features, announced at this week’s Code with Claude developer event. The basic idea: Claude, wrapped in a computer in the cloud, that you can spin up, scale, and manage as needed. Anthropic is taking on the infrastructure that kills most agent products, and making sure that it scales to meet the needs of agents running 24/7. On this week’s AI & I from Every 📧, I talk with Angela Jiang (Angela Jiang), head of product for the Claude platform, and Katelyn Lesse (Katelyn Lesse), head of engineering for the Claude platform, about what Anthropic is building and what it takes to make agents reliable in production. We get into: - Why the "build a generic harness, hot-swap any model behind it" playbook is already outdated. Angela points to eval data on Memory where the same task across different harnesses performed drastically differently. - The infrastructure wall every team hits in production—and why Katelyn thinks “my sandbox died and took the agent with it” is the real reason internal agents don't ship. - Why Anthropic is so bullish on using file systems and skills within Claude, including Angela's argument that those early design choices can compound for years. This is a must-watch for anyone trying to take an agent past the demo and into production. Watch below! Timestamps: How the Claude platform evolved from API to agents: 00:01:48 The primitives that make up Claude Managed Agents: 00:04:09 Why the harness and the model are becoming a single unit: 00:10:37 The infrastructure wall that kills most agent projects in production: 00:18:49 Why team agents need a different shape than individual productivity tools: 00:24:49 How Anthropic's legal team uses an agent to review marketing copy: 00:26:36 Using multi-agent orchestration for advisor strategies, adversarial pairs, and swarms: 00:34:24 How to measure agent success with outcome and budget as the end state: 00:35:50 What the platform looks like a year from now, when Claude writes its own harness: 00:39:11

Dan Shipper

66,862 Aufrufe • vor 4 Monaten

$SERV is the Fiverr/Shopify for AI agents. OpenServ provides a platform and marketplace to create, find, and employ AI agents. Here's why it can be a leading Agent marketplace and is undervalued compared to where it can go. ———————————————————— To put this in perspective, we will take this from the top down. Let's look at the valuation mismatch. → Shopify: $140B → Fiverr: $1.2B → $SERV: $37M AI agent platforms can completely replace these businesses. Why? → Can automate operations (i.e. store setup, inventory management, and customer support autonomously, etc.) → Agents can hyper-personalize the shopping experience → Store owners can own their data and have more control → Marketplaces are a cheaper/faster solution AI agents make things more convenient by performing tasks autonomously. They are inevitable. ———————————————————— The AI Agent market is projected to reach ~$50B by 2030. So the potential is MASSIVE. What makes me so bullish on $SERV specifically? There are 3 things: 1️⃣ The Tech They are targeting Web 2 businesses. This gives the platform the most upside potential imo, both in terms of adoption and valuation. These are some noteworthy highlights: → No-code AI Agent builder (anyone can build) → Builders can generate income using agents → ANY agent can cooperate with ANY agent through SERVs platform → Offers multi-agent collaboration, while allowing for human input/customization This sets them apart from other crypto-centric AI agent marketplaces. OpenServ allows you to create a team of agents to carry out complex tasks, all while automating the process and packaging their solution for Web 2 businesses. Simple tool, easy execution, and limitless productivity. Which business/individual wouldn't want to do MORE in LESS time at a rate MUCH LESS than solutions already available? ———————————————————— 2️⃣ The Team They have a stacked team. → Founders: Experience in businesses & startups → CTO: 20+ years of experience in ML/AI → CFO: ex-JP Morgan VP → CMO: ex-IBM AI & Blockchain Marketing Director Within the last few weeks/months, they have added a UI/UX designer, 4 more devs, and more devs + a product manager coming. You could have the best tech but the team is what determines its success. In this case, the team has the knowledge/experience to see this through. They have been building for a year and the progress made is a good sign of what's to come. ———————————————————— 3️⃣ The Tokenomics A percentage of transaction volume on the platform will be used to buy back and burn the $SERV token. This creates and maintains buy-side pressure and demand. To put that in perspective, Fiverr & Upwork had a combined transaction volume of $5B. The demand for Agents wont slow down anytime soon. Demand for AI agents will translate into demand for the token. I love deflation. It's simple, clean, and effective. ———————————————————— ➡️ Final Thoughts I've held on tight to my $SERV bag because the platform is launching in Q1. This will mark the beginning of their journey to the top. Agents are inevitable. Integration with Web 2 businesses is inevitable. And the platform launch is coming as alt szn is kicking off. The stars are aligning. At the same time, AI companies are already showing interest in the platform. Developers lead to more users, bringing monetization opportunities, which brings more developers, and so on. A powerfully designed flywheel. This is a new and exciting sector. I expect interest and liquidity to be focused on AI Agents and the infrastructure around them. Max opportunity is right here in this sector.
2:59

$SERV is the Fiverr/Shopify for AI agents. OpenServ provides a platform and marketplace to create, find, and employ AI agents. Here's why it can be a leading Agent marketplace and is undervalued compared to where it can go. ———————————————————— To put this in perspective, we will take this from the top down. Let's look at the valuation mismatch. → Shopify: $140B → Fiverr: $1.2B → $SERV: $37M AI agent platforms can completely replace these businesses. Why? → Can automate operations (i.e. store setup, inventory management, and customer support autonomously, etc.) → Agents can hyper-personalize the shopping experience → Store owners can own their data and have more control → Marketplaces are a cheaper/faster solution AI agents make things more convenient by performing tasks autonomously. They are inevitable. ———————————————————— The AI Agent market is projected to reach ~$50B by 2030. So the potential is MASSIVE. What makes me so bullish on $SERV specifically? There are 3 things: 1️⃣ The Tech They are targeting Web 2 businesses. This gives the platform the most upside potential imo, both in terms of adoption and valuation. These are some noteworthy highlights: → No-code AI Agent builder (anyone can build) → Builders can generate income using agents → ANY agent can cooperate with ANY agent through SERVs platform → Offers multi-agent collaboration, while allowing for human input/customization This sets them apart from other crypto-centric AI agent marketplaces. OpenServ allows you to create a team of agents to carry out complex tasks, all while automating the process and packaging their solution for Web 2 businesses. Simple tool, easy execution, and limitless productivity. Which business/individual wouldn't want to do MORE in LESS time at a rate MUCH LESS than solutions already available? ———————————————————— 2️⃣ The Team They have a stacked team. → Founders: Experience in businesses & startups → CTO: 20+ years of experience in ML/AI → CFO: ex-JP Morgan VP → CMO: ex-IBM AI & Blockchain Marketing Director Within the last few weeks/months, they have added a UI/UX designer, 4 more devs, and more devs + a product manager coming. You could have the best tech but the team is what determines its success. In this case, the team has the knowledge/experience to see this through. They have been building for a year and the progress made is a good sign of what's to come. ———————————————————— 3️⃣ The Tokenomics A percentage of transaction volume on the platform will be used to buy back and burn the $SERV token. This creates and maintains buy-side pressure and demand. To put that in perspective, Fiverr & Upwork had a combined transaction volume of $5B. The demand for Agents wont slow down anytime soon. Demand for AI agents will translate into demand for the token. I love deflation. It's simple, clean, and effective. ———————————————————— ➡️ Final Thoughts I've held on tight to my $SERV bag because the platform is launching in Q1. This will mark the beginning of their journey to the top. Agents are inevitable. Integration with Web 2 businesses is inevitable. And the platform launch is coming as alt szn is kicking off. The stars are aligning. At the same time, AI companies are already showing interest in the platform. Developers lead to more users, bringing monetization opportunities, which brings more developers, and so on. A powerfully designed flywheel. This is a new and exciting sector. I expect interest and liquidity to be focused on AI Agents and the infrastructure around them. Max opportunity is right here in this sector.

Chill

34,174 Aufrufe • vor 1 Jahr

From building internal AI labs to becoming CTO of Brex, James Reggio has helped lead one of the most disciplined AI transformations inside a real financial institution where compliance, auditability, and customer trust actually matter. We sat down with James Reggio to unpack Brex’s three-pillar AI strategy (corporate, operational, and product AI), how SOP-driven agents beat overengineered RL in ops, why Brex lets employees “build their own AI stack” instead of picking winners, and how a small, founder-heavy AI team is shipping production agents to 40,000+ companies. Reggio also goes deep on Brex’s multi-agent “network” architecture, evals for multi-turn systems, agentic coding’s second-order effects on codebase understanding, and why the future of finance software looks less like dashboards and more like executive assistants coordinating specialist agents behind the scenes. James Reggio Brex swyx Alessio Fanelli
0:44

From building internal AI labs to becoming CTO of Brex, James Reggio has helped lead one of the most disciplined AI transformations inside a real financial institution where compliance, auditability, and customer trust actually matter. We sat down with James Reggio to unpack Brex’s three-pillar AI strategy (corporate, operational, and product AI), how SOP-driven agents beat overengineered RL in ops, why Brex lets employees “build their own AI stack” instead of picking winners, and how a small, founder-heavy AI team is shipping production agents to 40,000+ companies. Reggio also goes deep on Brex’s multi-agent “network” architecture, evals for multi-turn systems, agentic coding’s second-order effects on codebase understanding, and why the future of finance software looks less like dashboards and more like executive assistants coordinating specialist agents behind the scenes. James Reggio Brex swyx Alessio Fanelli

Latent.Space

32,800 Aufrufe • vor 8 Monaten

We asked a dozen DevTool founders from companies like RevenueCat, Greptile, Firecrawl, Infisical, ollama, Resend, Mintlify, Unsloth AI, Porter, and recall.ai, about the state of AI agents and the future of software engineering. In this episode of Founder FAQ, we covered everything from agents as customers and the end of coding, to advice for founders starting out and what they're most excited about going forward. Their answers might surprise you. 00:00 – Meet the Founders 03:00 – Building for Agents First 04:22 – Biggest Early Mistakes 07:15 – Do Founders Still Write Code? 09:22 – Most Unexpected AI Discoveries 12:09 – What's Underrated Right Now 14:38 – Predictions & What's Next
20:29

We asked a dozen DevTool founders from companies like RevenueCat, Greptile, Firecrawl, Infisical, ollama, Resend, Mintlify, Unsloth AI, Porter, and recall.ai, about the state of AI agents and the future of software engineering. In this episode of Founder FAQ, we covered everything from agents as customers and the end of coding, to advice for founders starting out and what they're most excited about going forward. Their answers might surprise you. 00:00 – Meet the Founders 03:00 – Building for Agents First 04:22 – Biggest Early Mistakes 07:15 – Do Founders Still Write Code? 09:22 – Most Unexpected AI Discoveries 12:09 – What's Underrated Right Now 14:38 – Predictions & What's Next

Y Combinator

27,672 Aufrufe • vor 4 Monaten

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
46:57

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 Aufrufe • vor 6 Monaten