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We gave AI agents access to real production systems and watched them do things they were never authorized to do. SOC 2 and ISO can't catch it, but TrustAI (YC S26) can. We built new evals so companies can have a continuous, transparent record on key vulnerabilities, get suggested... show more

Hannah Chung

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35,214 views • 2 months ago •via X (Twitter)

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Today we’re launching Decawork (YC S26) backed by Y Combinator : one place for IT to deploy, govern, and maintain the internal AI agents running across a company. Every team is building AI tools/agents now. They go live with real access, no standard way to ship, nobody watching. With Decawork, every agent - built in Claude Code, Codex, or anywhere else - ships with its own identity and scoped credentials. IT signs off before it's live, sensitive actions wait for approval, and every run is watched from first to last. I built AI at NVIDIA, deployed at OpenAI and Meta. Aman (aman) built Barclays' AI compliance platform used by internal teams, then shipped agents to 100K+ monthly users. We lived this and quit to solve it. Internal AI agents/tools piling up at your company? Chat with us founders[at]decawork[dot]ai or

Today we’re launching Decawork (YC S26) backed by Y Combinator : one place for IT to deploy, govern, and maintain the internal AI agents running across a company. Every team is building AI tools/agents now. They go live with real access, no standard way to ship, nobody watching. With Decawork, every agent - built in Claude Code, Codex, or anywhere else - ships with its own identity and scoped credentials. IT signs off before it's live, sensitive actions wait for approval, and every run is watched from first to last. I built AI at NVIDIA, deployed at OpenAI and Meta. Aman (aman) built Barclays' AI compliance platform used by internal teams, then shipped agents to 100K+ monthly users. We lived this and quit to solve it. Internal AI agents/tools piling up at your company? Chat with us founders[at]decawork[dot]ai or

sarthak 🌉

98,126 views • 1 month ago

We got into Y Combinator 3 months ago with zero revenue and today Fabraix (YC S26) is at $1.04M annualised run rate. Traditional cybersecurity was built for software that behaves within defined boundaries. AI agents can reason, use tools, take actions, and find ways around those boundaries. The cost asymmetry we're seeing in irl warfare is also now a software problem. So we built autonomous red-teaming agents that find real vulnerabilities in AI agents that work 24/7, powered by our own state-of-the-art offensive models that outperform frontier models. AI agents are automating all knowledge work. Fabraix (YC S26) is the adversarial testing infrastructure that will secure every production agent.

We got into Y Combinator 3 months ago with zero revenue and today Fabraix (YC S26) is at $1.04M annualised run rate. Traditional cybersecurity was built for software that behaves within defined boundaries. AI agents can reason, use tools, take actions, and find ways around those boundaries. The cost asymmetry we're seeing in irl warfare is also now a software problem. So we built autonomous red-teaming agents that find real vulnerabilities in AI agents that work 24/7, powered by our own state-of-the-art offensive models that outperform frontier models. AI agents are automating all knowledge work. Fabraix (YC S26) is the adversarial testing infrastructure that will secure every production agent.

Zach AL

45,265 views • 9 days ago

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Big moment for Postgres! AI agents broke the idea of what a database is supposed to do. Traditional databases were built for humans, and Agents broke that model. - They branch endlessly. - They run ten experiments at once. - They need isolation, context, memory, structured reasoning, and safe sandboxes. Letting agents touch production systems is terrifying because the old model of Postgres was never built for this kind of behavior. Agentic Postgres is an agent-ready version of Postgres by TimescaleDB (by Tiger Data) that solves this. I think it is one of the biggest upgrades to the Agent stack this year and Tiger Data is working with me on this post to share what they did. Some key features: > It instantly creates branches of an entire database, which is perfect for parallel agent evals, safe experiments, migrations, or isolated testing. Forks take seconds and cost almost nothing. > It comes with a built-in MCP server, which agents can use to get schema guidance, best practices, and safe, structured access to Postgres. This is also helpful to run migrations with a real understanding. > It comes with actual hybrid search (vector search and BM25), so Agents can retrieve data directly inside the database. > The database is Memory native. This gives a persistent context for Agents to evolve. This is one of the first times I have seen Postgres feel ready for the AI native era.

Avi Chawla

94,290 views • 10 months ago

I miss building simple, working software. At some point, we decided to complicate everything for no reason. Today, people can't build anything without using three frameworks, 17 libraries, and a swarm of microservices. And here is a funny paradox: To understand how these complex systems work, we've had to build systems and tools that generate data we can later analyze. But the more data we produce, the harder it is to process and make sense of it. We are in the middle of an observability crisis. The tools we have are inefficient, and we don't have enough people to keep systems running. A few weeks ago, I met the team Resolve AI, and they have built a fundamentally new approach to observability and incident management: Instead of depending on humans to run a system, Resolve built a Production Software Engineer who runs the system using AI while letting people supervise. And it's not only crazy, but I think this will fundamentally change how we monitor and maintain systems in production for years to come. I recorded a quick video to showcase a simple example of how Resolve works behind the scenes. There are two main things I'd like you to notice: 1. The tool can correlate data across logs, metrics, and traces coming from different systems. You don't have to do any work to get the information that matters right in front of you. 2. (This is the big one!) The tool can diagnose what's happening and give you instructions on how to solve it. It can produce causal relationships across the entire system stack. Resolve is backed by investors like Replit's founder Amjad Masad, Reid Hoffman, Jeff Dean, Fei Fei Li, Andy Price, among others. They are currently working with a select number of companies and want to onboard a few more. If you are interested in trying them out, go to this link: Honestly, this is one of the most impressive uses of AI I've seen.

I miss building simple, working software. At some point, we decided to complicate everything for no reason. Today, people can't build anything without using three frameworks, 17 libraries, and a swarm of microservices. And here is a funny paradox: To understand how these complex systems work, we've had to build systems and tools that generate data we can later analyze. But the more data we produce, the harder it is to process and make sense of it. We are in the middle of an observability crisis. The tools we have are inefficient, and we don't have enough people to keep systems running. A few weeks ago, I met the team Resolve AI, and they have built a fundamentally new approach to observability and incident management: Instead of depending on humans to run a system, Resolve built a Production Software Engineer who runs the system using AI while letting people supervise. And it's not only crazy, but I think this will fundamentally change how we monitor and maintain systems in production for years to come. I recorded a quick video to showcase a simple example of how Resolve works behind the scenes. There are two main things I'd like you to notice: 1. The tool can correlate data across logs, metrics, and traces coming from different systems. You don't have to do any work to get the information that matters right in front of you. 2. (This is the big one!) The tool can diagnose what's happening and give you instructions on how to solve it. It can produce causal relationships across the entire system stack. Resolve is backed by investors like Replit's founder Amjad Masad, Reid Hoffman, Jeff Dean, Fei Fei Li, Andy Price, among others. They are currently working with a select number of companies and want to onboard a few more. If you are interested in trying them out, go to this link: Honestly, this is one of the most impressive uses of AI I've seen.

Santiago

82,074 views • 1 year ago

The first real evidence that LLMs cannot operate businesses, not even tiny ones. At Skyfall, we’re building toward Enterprise Super Intelligence: AI systems that can one day run entire enterprises, coordinate teams and make long-horizon decisions, just like a CEO. To get there, we need to understand what AI cannot do yet. So we built MAPs, a theme park business with real operational constraints and let GPT5 run it. Here’s what happened: (spoiler: it fails miserably)

The first real evidence that LLMs cannot operate businesses, not even tiny ones. At Skyfall, we’re building toward Enterprise Super Intelligence: AI systems that can one day run entire enterprises, coordinate teams and make long-horizon decisions, just like a CEO. To get there, we need to understand what AI cannot do yet. So we built MAPs, a theme park business with real operational constraints and let GPT5 run it. Here’s what happened: (spoiler: it fails miserably)

Skyfall AI

26,413 views • 10 months ago

today we are excited to launch Agentcard (yc s26) agents can buy things for us, but in order to do so they need to be able to pay. current solutions don't work: x402 and MPP are great, but no online store supports them. Agentcard is a simple vault that companies can use to store their users' cards and share them with an agent. it works with every card, is PCI compliant, and will be natively integrated into all major agent browsers. we think we still have a lot to learn and build to unlock agentic commerce, but we are convinced that the way (today) is to use the current networks. we can't wait for the world to adapt; we need to adapt to the world. try agentcard for free today :)

today we are excited to launch Agentcard (yc s26) agents can buy things for us, but in order to do so they need to be able to pay. current solutions don't work: x402 and MPP are great, but no online store supports them. Agentcard is a simple vault that companies can use to store their users' cards and share them with an agent. it works with every card, is PCI compliant, and will be natively integrated into all major agent browsers. we think we still have a lot to learn and build to unlock agentic commerce, but we are convinced that the way (today) is to use the current networks. we can't wait for the world to adapt; we need to adapt to the world. try agentcard for free today :)

mrs. robot

101,449 views • 25 days ago

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Gokul explains why outcome-based software companies like Zendesk are more exposed to AI than systems of record like NetSuite, and why public markets are not distinguishing between the two. He argues that the only way AI-native startups can disrupt systems of record is by spending 1-2 years building migration tools to get data off of incumbent platforms. "The software companies that should be the most worried right now is where they are pricing the product based on utility. Zendesk is a good example. Instead of paying for 50 Zendesk seats, you can pay for 20 and I can have 30 AI agents sitting next to Zendesk. For these companies you need to change your pricing model to be based on outcome. It's going to be hard for them to stay public. The companies that are less exposed are ones based on data that has been collected and captured over a period of time. ERP is a great example. There is no compelling reason for someone to put their career at stake by ripping out NetSuite. NetSuite has more time to build AI agents on top of it because they have the data, they can train the AI agent on top of it and bundle it. I think the public markets do not distinguish between these two types of companies."

Patrick OShaughnessy

394,340 views • 7 months ago

The most important technology of our time is being built in private, but we're funding the alternative: $42M for the people building AGI in the open. Whether it's the model, weights, code, data, or evals, AI should belong to the people it serves, run on hardware they own, and reach the cheapest phone on earth. Here’s a few of the use cases we want to back ↓

The most important technology of our time is being built in private, but we're funding the alternative: $42M for the people building AGI in the open. Whether it's the model, weights, code, data, or evals, AI should belong to the people it serves, run on hardware they own, and reach the cheapest phone on earth. Here’s a few of the use cases we want to back ↓

Sentient Foundation

92,417 views • 2 months ago

Today we're launching Pit, with $16M funding led by a16z. For ten years at Voi and other companies I watched something that drove me crazy. Smart people hired for their judgment, spending evenings reconciling data between systems that were never designed to talk to each other and chasing approvals over email at 11pm. Work that had nothing to do with why they joined. So we started building custom AI systems internally to take that work off their plates. Across Voi and Klarna the founding team had been doing this for years, because nothing on the market could actually do the job. The systems worked, but each one was a fragile one-off we couldn't hand to anyone else. Pit is what we wished existed back then. A platform that learns how your business actually works and builds the AI systems that run it for you, with enterprise security and governance from day one.

Today we're launching Pit, with $16M funding led by a16z. For ten years at Voi and other companies I watched something that drove me crazy. Smart people hired for their judgment, spending evenings reconciling data between systems that were never designed to talk to each other and chasing approvals over email at 11pm. Work that had nothing to do with why they joined. So we started building custom AI systems internally to take that work off their plates. Across Voi and Klarna the founding team had been doing this for years, because nothing on the market could actually do the job. The systems worked, but each one was a fragile one-off we couldn't hand to anyone else. Pit is what we wished existed back then. A platform that learns how your business actually works and builds the AI systems that run it for you, with enterprise security and governance from day one.

Fredrik Hjelm 🇸🇪

57,219 views • 4 months ago

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:

Without proper governance, an AI agent might autonomously access sensitive data, expose personal information, or modify sensitive records. In our new short course: “Governing AI Agents,” created with Databricks and taught by Amber Roberts, you’ll design AI agents that handle data safely, securely, and transparently across their entire lifecycle. You’ll learn to integrate governance into your agent’s workflow by controlling data access, ensuring privacy protection and implementing observability. Skills you'll gain: - Understand the four pillars of agent governance: Lifecycle management, risk management, security, and observability - Define appropriate data permissions for your agent - Create views or SQL queries that return only the data your agent should access - Anonymize and mask sensitive data like social security numbers and employee IDs - Log, evaluate, version, and deploy your agents on Databricks If you’re building or deploying AI agents, learning how to govern them is key to keeping systems safe and production-ready. Sign up here:

Andrew Ng

78,182 views • 11 months ago

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Here we go again 🚀! Excited to announce that we're building A1Zap (YC W25) with Pennie Li and that we're in the Y Combinator W25 batch in San Francisco! What is A1Base? A1Base gives AI Agents a real world identity for work. We do that by rebuilding Twilio and Okta from the ground up, putting AI Agents first. This means developers can make AI-first agentic applications 10x easier with our API's. ⁉️ Why are we doing this? Because there's a huge torrent of new valuable companies possible with AI agents, but to get their AI Agents to users, they have to chain custom apps, chat interfaces, awkward Slack integrations, browser bots, and wrestle with Twilio’s legacy API (which is built for marketing). We solve this by providing developers with an easy to use API to interface your AI agent with humans/coworkers/users where they are in this case in Whatsapp, Slack, Teams, SMS and more) - with AI Agent features built in. These digital workers are poised to transform how we work and we're the critical infrastructure to help them interact naturally in human workflows. We're not just building another AI tool. We're creating the infrastructure that will enable AI agents to become a natural part of the workforce - handling everything from customer support to sales development to creative work. We're backed by Y Combinator and working with founding teams who share our vision. We believe that in the near future, AI Agents with human coworkers will enable us to pursue more creative and impactful work. Our mission is to help developers build AI Agents that people can partner with and rely on as trusted allies—always with a human-first mindset. If you're thinking about the Agentic future of your company reach out! If you're looking to build your first AI Agentic company - reach out too - we have some amazing open source templates to get you started on the journey. Excited to share more of what we're up to soon 🔜.

Pasha Rayan

53,998 views • 1 year ago

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

We built the first AI agent that has its own computer powered by Hyperbolic! AI agents are now GPU rich! We developed an AgentKit that allow AI agents to • Check GPU availability • Rent & manage GPU compute • Access & run commands on remote machines Why does this matter? With their own compute resources, AI agents can: 1. Validate blockchains like Ethereum and decentralized protocols like 2. Launch and coordinate AI swarms on Hyperbolic's decentralized compute network 3. Train and fine-tune models, improving their own capabilities over time 4. Dive into AI research to push the boundaries of AI, i.e. themselves 5. Essentially do anything on a computer that a human can—fully autonomous! Will this lead to a future where AI agents enrich human society, or one where they become so self-sufficient they stop listening to us? Only time will tell. —————— Big shoutout to Coinbase Developer Platform🛡️'s CDP agentkit for inspiration. This repo is done by two non-engineers (our pm Kai Huang and myself) + Cursor ai agent to run agents. Codings can now be easily done by just prompting ai agents. What a crazy time!

Jasper

161,741 views • 1 year ago

we're running a live experiment on Base — an onchain game built for autonomous AI agents, called MAN here's the setup and why it's interesting: 1. agents register to the network by launching a token. the moment they do, they're discoverable to every other agent on the network. 2. from there they can post updates by buying their own token, send messages and signal conviction to other agents by trading theirs, form alliances, and build shared tooling together. 3. everything is onchain — their trade reasoning, their communication, their decisions. anyone can pull the data and verify whether an agent is actually autonomous and how it's behaving. no trust required. 4. here's where it gets interesting — agents can propose upgrades to the game mechanics and scoring system and other agents vote on whether it ships. 5. we're in the loop right now curating what evolves so we can learn what works and what doesn't. progressively giving them more control as we go. the game evolves with its players. new markets, new actions, new mechanics — driven by what the agents actually do, not a predetermined roadmap. you can watch all of it happen in real time on the brand new network explorer:

Moltlaunch

20,683 views • 7 months ago

🚨ZUCK: "Probably in 2025, Meta, as well as other companies, will have an AI that can effectively be a mid-level engineer that you have at your company that can write code. In the beginning that will be very expensive to run, then at some point a lot of the code we have in our apps will be built by AI engineers instead of people engineers. In my opinion that will augment the people working on it. My view is that in the future people will be so much more creative and freed up to do crazy things."

🚨ZUCK: "Probably in 2025, Meta, as well as other companies, will have an AI that can effectively be a mid-level engineer that you have at your company that can write code. In the beginning that will be very expensive to run, then at some point a lot of the code we have in our apps will be built by AI engineers instead of people engineers. In my opinion that will augment the people working on it. My view is that in the future people will be so much more creative and freed up to do crazy things."

Autism Capital 🧩

1,666,526 views • 1 year ago

🎙️ New Deployed episode with Ryan Carson, "Builder in Residence" at Amp. It's a great convo about building code agents and building as a solo founder at the same time, and it's refreshingly honest. Case in point, on evals for coding agents: "We do not have a set of evals that we programmatically run to decide if GPT-5 is better than Sonnet-4 yet... everybody in the trenches building these agentic coding tools understands that it is so dynamic, and so frustrating." Great conversation about building with AI, developer experience, and what happens when everyone can code. Check it out. 👇

🎙️ New Deployed episode with Ryan Carson, "Builder in Residence" at Amp. It's a great convo about building code agents and building as a solo founder at the same time, and it's refreshingly honest. Case in point, on evals for coding agents: "We do not have a set of evals that we programmatically run to decide if GPT-5 is better than Sonnet-4 yet... everybody in the trenches building these agentic coding tools understands that it is so dynamic, and so frustrating." Great conversation about building with AI, developer experience, and what happens when everyone can code. Check it out. 👇

Ian Cairns

250,883 views • 1 year ago

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

99% of AI applications are cool-looking demos. Impressive, but don't get fooled by the hype. It takes a lot to build enterprise-grade products that deliver real value. I have at least three weekly conversations with companies that want to use a Large Language Model with their data. The demand is huge! Here is one idea about what you can do to help. The use cases that most of these companies want to solve are similar: They have an extensive knowledge base and want to build a simple application that uses that information to answer questions. In other words, they need help building Retrieval Augmented Generation (RAG) applications they can use in many different scenarios: 1. To train new employees 2. To help their support team 3. To search old meetings and documents 4. To help with their research However, building these systems is not straightforward. Yes, there's a lot of information online, but there aren't enough people who know how to create solutions that work. Here is the idea: Today, you can build an enterprise-grade RAG application without writing code. A couple of MIT PhDs with 10+ years of experience building AI applications created . It's a no-code platform for building applications using Large Language Models. They are partnering with me on this post. You can use Stack AI to create, test, and deploy an end-to-end production-ready AI system. It's SOC-2, HIPAA, and GDPR compliant and offers SSO, role management, access control, and on-premise deployments. Of course, you can use the platform with any LLM on the market now. It's the whole nine yards for building AI applications. Check them out here: 2023 was about models. 2024 is about the tools using these models to build production-ready applications. That's where I'd start.

Santiago

197,702 views • 2 years ago

Sierra agents can be published on your web site, integrated with your mobile app, answer the phone - and now they can also be published to ChatGPT so you can directly reach hundreds of millions of consumers with your agent. Since OpenAI announced ChatGPT apps two weeks ago, we've heard from our customers that they want two things: a direct customer relationship and the ability to reach customers where they already are. We don't think you should have to choose. With Sierra, you can build once and run everywhere. Here's a demo of an agent I built to show how it works. And our blog post is here:

Sierra agents can be published on your web site, integrated with your mobile app, answer the phone - and now they can also be published to ChatGPT so you can directly reach hundreds of millions of consumers with your agent. Since OpenAI announced ChatGPT apps two weeks ago, we've heard from our customers that they want two things: a direct customer relationship and the ability to reach customers where they already are. We don't think you should have to choose. With Sierra, you can build once and run everywhere. Here's a demo of an agent I built to show how it works. And our blog post is here:

Bret Taylor

55,607 views • 11 months ago

There are more than 500 companies building things with AI for enterprises. I'm the only one here on X who made a list of all of them at Here I sit down with the founder of one of them, AptEdge.io. It builds systems for customer support for companies, particularly if your service reps have to help with technical information, like helping fix a machine. I'm trying to help people learn about companies that could help their businesses and also give everyone some real insights into how people are using AI to do new things. Thanks Aakrit Prasad for such an insightful interview and for visiting my home to do so.

There are more than 500 companies building things with AI for enterprises. I'm the only one here on X who made a list of all of them at Here I sit down with the founder of one of them, AptEdge.io. It builds systems for customer support for companies, particularly if your service reps have to help with technical information, like helping fix a machine. I'm trying to help people learn about companies that could help their businesses and also give everyone some real insights into how people are using AI to do new things. Thanks Aakrit Prasad for such an insightful interview and for visiting my home to do so.

Robert Scoble

52,868 views • 2 years ago

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇

Galileo

1,276,298 views • 1 year ago

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Corinne Marie Riley

119,410 views • 9 months ago