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Many software products are adding AI copilots, mostly assistants that can find answers, take actions, etc. But just like how coding is moving to autonomous agents, a similar shift will happen in-product. We've pioneered the use of long-running agents via Decagon Duet. [1/4]👇

Jesse Zhang

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10,520 views • 5 months ago •via X (Twitter)

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The new Factory software AI agents just combined OpenAI’s Codex, Devin, and Cursor into one product. They are fully autonomous agents called Droids that perform deep code research, technical docs, and do autonomous end-to-end coding. Here’s how it works (with real examples)👇:

The new Factory software AI agents just combined OpenAI’s Codex, Devin, and Cursor into one product. They are fully autonomous agents called Droids that perform deep code research, technical docs, and do autonomous end-to-end coding. Here’s how it works (with real examples)👇:

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The SaaSpocalypse narrative is starting to make less and less sense. Software companies like $PLTR, $NOW, and $CRM are getting punished because investors believe AI will replace SaaS. Jensen Huang disagrees. “Claude can write software.” That’s true. But Jensen argues that enterprises aren’t going to build their own software. They’re going to deploy AI agents that use software tools to get work done. “Agents won’t replace tools. Agents will use tools.” NVIDIA is already seeing this happen. According to Jensen, the company has deployed so many AI agents that tool usage is actually increasing. That’s the exact opposite of the SaaSpocalypse thesis.

The SaaSpocalypse narrative is starting to make less and less sense. Software companies like $PLTR, $NOW, and $CRM are getting punished because investors believe AI will replace SaaS. Jensen Huang disagrees. “Claude can write software.” That’s true. But Jensen argues that enterprises aren’t going to build their own software. They’re going to deploy AI agents that use software tools to get work done. “Agents won’t replace tools. Agents will use tools.” NVIDIA is already seeing this happen. According to Jensen, the company has deployed so many AI agents that tool usage is actually increasing. That’s the exact opposite of the SaaSpocalypse thesis.

Daniel

125,274 views • 2 months 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

40,857 views • 1 day ago

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

We're entering a new era of software where a single person, working with AI agents, can build products that previously required entire teams. In this episode of the Lightcone Podcast, they break down the rise of AI coding agents, "tokenmaxxing", and the emerging workflows behind tools like Claude Code and OpenClaw. They discuss why AI systems today feel less like productivity tools and more like collaborators, why the future of AI should be personal and user-controlled, and how founders are starting to build software in completely new ways. 00:00 — Will you control your AI? 00:47 — Coding again after 13 years 01:56 — Rebuilding a startup with Claude Code 05:50 — Software that thinks like a journalist 07:09 — The rise of “tokenmaxxing” 10:07 — The accidental creation of GStack 14:21 — The workflow behind 400x output 20:59 — Thin Harness, Fat Skills 24:35 — AI agents are like Ferraris 27:12 — The future of personal AI 38:37 — Buying back time with tokens

Y Combinator

67,145 views • 4 months ago

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Jensen Huang just said this is the greatest era in history to build software. AI agents will not kill software. They will do the exact opposite: create a massive new wave of software demand. at NVIDIA GTC Taipei 2026 "Click and type. We now replace that with explaining to the AI what we want, our intent, and the AI generates the code or uses tools to produce the necessary output. This is how computers are going to work in the future. This is Agentic AI. For two years, we've been building toward this, and now it has arrived. One of the big breakthroughs, of course, is tool use. A lot of people have said, “Jensen, AI is coming. Agentic AI is coming. Therefore, all the software companies are going to go out of business.” This is exactly the opposite. Because there are going to be so many agents, the world is no longer limited by the number of people. Therefore, those agents are going to use more tools than ever. This is actually an incredible time to be a software company. But the software has to be presented to the agent in a way that the agent can use it. This is a big breakthrough. And in fact, what we have done, as you know, what Nvidia’s treasure..." ---- From 'NVIDIA' YT channel (link in comment)

Rohan Paul

16,584 views • 3 months ago

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.

Over the past couple of years, you’ve heard us reference “agents” and “agentic capabilities,” nebulous concepts that are designed to help you with coding, booking trips, and other complex multi-step tasks. But, what actually IS an agent? We think of AI agents as systems that combine the intelligence of advanced AI models with access to tools so they can take actions on your behalf, under your control. Whether it’s a coding agent or our recently unveiled experimental tool Gemini Agent in the Google Gemini, these are systems that will meet you where you are, in the products that you love, to help take the tedious tasks you want off your plate.

Google AI

120,833 views • 9 months ago

Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done."

Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done."

TBPN

58,374 views • 7 months ago

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

The rules of professional product development are being rewritten in real time. - PMs and designers can ship software as easily as engineers. - Software is no longer just built for humans—it’s also built for agents as first-class citizens. To better understand how we build products in this world, I invited Mike Krieger (Mike Krieger) on Every 📧’s AI & I podcast. Mike cofounded Instagram and is now a member of the technical staff at Anthropic, co-leading Anthropic Labs, their internal incubator for experimental products. He's been at the frontier of two transformative technology waves: mobile/social and now agent-native software. We discussed: - How to build a truly agent-native product. The best products today, like Claude Code, allow users to do things that their creators never intended. But that requires hard trade-offs between freedom and safety/reliability for frontier products, an issue that Mike's team is learning how to solve. - What's different about building now versus building Instagram. At Instagram, it took months to hit dead ends and learn what to cut. Now, that cycle runs in hours. - The trap of building too much, too fast with agents. You can go from idea to a nearly-shipped product in a day, but that process doesn’t give you the incremental feedback that used to tell you what not to build. The models are great at adding features, but can create a product that lacks coherence. - How Anthropic Labs structures product teams. New product experiments are led by only two people, usually a product manager or designer paired with an engineer. Mike says bigger teams tend to be too slow because of coordination costs. - Why you need to throw out your product and start over every three to six months. AI progress means most of your harness will be outdated quickly—the best teams build this into their product strategy. And much more! You should watch this one. Timestamps Introduction: What's gotten easier—and what hasn't—about building products in the age of AI: Why vibe coding creates "indoor trees": How rewrites have become a normal part of the development process: What "agent native" product design means: How Mike's labs team is structured and the cofounder model: The best signal for a product bet is someone with "break through walls" conviction: Navigating enterprise customers while keeping pace with rapid AI change: OpenClaw, personal agents, and the product question defining 2026:

Dan Shipper 📧

58,849 views • 5 months ago

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z speedrun – If you’re building the next generation of AI products, apply online.

Proactive AI Agents Today’s AI products are reactive. You give the model a prompt, it responds with an answer. These are useful, but I’m excited about products that take this further and shift the paradigm from “ask → answer” to “observe → act." These agents will continuously monitor context in the background across all of your connected tools and data, predict what matters, and take action before being asked to do so – much like a human does. So instead of you prompting the model, the model will start prompting you. Examples here could include agents that remind you about tasks you forgot to complete, resolve customer issues before support tickets are filed, or debug and ship code fixes automatically. This shift represents a new paradigm where AI products behave more like humans and less like tools. We’re already starting to see this dynamic with products like OpenClaw, Poke, and more - and we’ve only scratched the surface of capabilities here. We’re accepting applications for the next cohort of a16z speedrun – If you’re building the next generation of AI products, apply online.

Kenan Saleh

129,165 views • 4 months ago

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 views • 5 months ago

Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done." From his appearance on the show in January.

Sequoia partner Pat Grady says software is shifting from apps that demand attention to agents that work quietly in the background. This shift will change what moats will look like, and will be especially hard for incumbents to deal with. "It's two very different business paradigms," he says. "In an era of apps, you want a lot of surface area with your customers, you want them to spend a lot of time in your product." "In an era of agents, things can just be running passively in the background. The amount of surface area you have with your customers, the amount of time they might spend in your product might be de minimis. So the nature of the moat that you [will need to build] is different." "I think we'll see a lot of companies in the coming years able to live in the software world and have some of the workflows people are accustomed to. And then separately they can deploy these passive agents that kind of function as coworkers who just come back to you when things are done." From his appearance on the show in January.

TBPN

47,208 views • 5 months ago

Sam Altman, CEO of OpenAI: "Every night, I have a few thousand agents running with GPT-6 Astra" One person → Thousands of Agents → Overnight Workflows → Autonomous Systems A few thousand AI agents working through the night, monitored entirely from his phone In 53 minutes, he shows what one person can build today and how long it used to take an entire team This is worth more than most paid AI engineering courses Watch him explain how Then read the full breakdown below ↓

Sam Altman, CEO of OpenAI: "Every night, I have a few thousand agents running with GPT-6 Astra" One person → Thousands of Agents → Overnight Workflows → Autonomous Systems A few thousand AI agents working through the night, monitored entirely from his phone In 53 minutes, he shows what one person can build today and how long it used to take an entire team This is worth more than most paid AI engineering courses Watch him explain how Then read the full breakdown below ↓

kaori

25,367 views • 3 days ago

Anthropic engineer: "99% of people use Claude Code like Google, and only 1% are running swarms of self-learning Claude agents I'm running 100+ agents in a loop. I have Chief agent, PM agents - they manage the whole team" in a 30-minute workshop, an Anthropic engineer revealed how to get max value from Claude Code at min. cost this is worth more than another $500 vibe-coding course watch today, then read how to build self-improving agentic systems with Fable in the article below

Anthropic engineer: "99% of people use Claude Code like Google, and only 1% are running swarms of self-learning Claude agents I'm running 100+ agents in a loop. I have Chief agent, PM agents - they manage the whole team" in a 30-minute workshop, an Anthropic engineer revealed how to get max value from Claude Code at min. cost this is worth more than another $500 vibe-coding course watch today, then read how to build self-improving agentic systems with Fable in the article below

Codez

261,964 views • 1 day ago

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

AI coding agents aren't just about autocorrect, but how do you get the best coding experience with an AI-powered coding agent? In our new short course, Build Apps with Windsurf’s AI Coding Agents, you'll learn how to build, debug, and deploy applications with agentic AI-powered integrated development environment (IDE). AI coding agents, like Codeium's @windsurf, don’t just suggest code, they analyze your codebase, track changes, retrieve relevant information, and apply updates across multiple files. They can help debug, refactor, and even modernize legacy frameworks. But to use them effectively, you need the right approach. This new course shows you how to: 🛠️ Use AI agents to build and refine applications, like a Wikipedia analysis app. 🐞 Debug and refactor JavaScript with AI-assisted automation. 🔍 Understand how search and retrieval power AI coding agents. 🤖 Guide an AI agent effectively—prompting, iterating, and correcting when needed. Taught by Anshul Ramachandran (Anshul Ramachandran), this course gives you hands-on coding experience, insights into how these AI systems work under the hood, and best practices to improve your development workflow. 🔗 Enroll for free:

DeepLearning.AI

23,255 views • 1 year ago

Finally, a browser automation framework for Agents that actually works in production (open-source)! Typical browser automation tools like Selenium or Playwright require you to hard-code automation. These are brittle since one change in the website can disrupt the full workflow. On the other hand, high-level Agents like OpenAI Operator can be unpredictable in production. Stagehand is an open-source framework that bridges the gap between: - brittle traditional automation like Playwright, Selenium, etc. and - unpredictable full-agent solutions like OpenAI Operator. Key features: - Use AI when you want to navigate unfamiliar pages. - Use code (Playwright) when you know exactly what to do. - Preview AI actions before running them. - Cache repeatable actions to save tokens. - Compatible with SOTA computer use models with just one line of code. - Available in both Python and TypeScript SDK. Stagehand also has an open-source MCP server. Find a link to the repo in the comments! 👉 Over to you: Would you use this instead of Selenium?

Finally, a browser automation framework for Agents that actually works in production (open-source)! Typical browser automation tools like Selenium or Playwright require you to hard-code automation. These are brittle since one change in the website can disrupt the full workflow. On the other hand, high-level Agents like OpenAI Operator can be unpredictable in production. Stagehand is an open-source framework that bridges the gap between: - brittle traditional automation like Playwright, Selenium, etc. and - unpredictable full-agent solutions like OpenAI Operator. Key features: - Use AI when you want to navigate unfamiliar pages. - Use code (Playwright) when you know exactly what to do. - Preview AI actions before running them. - Cache repeatable actions to save tokens. - Compatible with SOTA computer use models with just one line of code. - Available in both Python and TypeScript SDK. Stagehand also has an open-source MCP server. Find a link to the repo in the comments! 👉 Over to you: Would you use this instead of Selenium?

Avi Chawla

101,419 views • 1 year ago

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

New Short Course: Building AI Browser Agents! Learn how to build AI agents that interact and take actions on websites in this course, created in partnership with and taught by and @namangarg0, Co-founders of AGI Inc. AI browser agents can log into websites, fill out forms, click through web pages, or even place orders online for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take action. With the complexity of webpages and multiple possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. Because these agents run long action sequences, a single error—like clicking the wrong button or misreading a field—can lead to unexpected outcomes or errors that compound over time. In this course, you'll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction. In detail, you'll: - Learn what web agents are, how they automate tasks online, their architecture, key components, limitations, and an overview of their decision-making strategies. - Build a web agent that can scrape website and return course recommendations in a structured output format. - Build an autonomous web agent that can execute multiple tasks, such as finding and summarizing webpages, filling out a form, and signing up for a newsletter. - Explore AgentQ, a framework that enables agents to self-correct by combining Monte Carlo Tree Search (MCTS), a self-critique mechanism for continuous improvement, and Direct Preference Optimization (DPO). - Deep dive into MCTS, learn how it finds an effective path, illustrated by an example of Gridworld animation, and use AgentQ to complete web tasks. - Understand AI agents' current state and future directions—including key factors shaping their evolution, such as hardware, algorithm innovation, and data availability. By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable. Please sign up here:

Andrew Ng

186,182 views • 1 year ago

SpaceXAI engineer (ex-Cursor): "99% of people still use GrokBot like Google and only 1% are building teams of self-learning agents i'm running 10-20 Grok cloud agents in a loop. I have a Chief of Staff, a PM agent and 15+ worker agents" in a 20-minute workshop, a SpaceXAI engineer showed how to build cloud agents that will work for you 24/7 this replaced a $500 agentic engineering course watch today, then read the article below on how to use GrokBot at 100% of its power

SpaceXAI engineer (ex-Cursor): "99% of people still use GrokBot like Google and only 1% are building teams of self-learning agents i'm running 10-20 Grok cloud agents in a loop. I have a Chief of Staff, a PM agent and 15+ worker agents" in a 20-minute workshop, a SpaceXAI engineer showed how to build cloud agents that will work for you 24/7 this replaced a $500 agentic engineering course watch today, then read the article below on how to use GrokBot at 100% of its power

Codez

2,310,486 views • 2 days ago

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.

The coming merger of AI agents with the physical world. @iddris, founder/CEO of Open Matter and I have a wide-ranging conversation about how the next platform shift may not be just AI chat, robotics, smart glasses, or autonomous cars separately. It may be the connective tissue between all of them. This video teaches viewers that the next wave of AI agents will not just operate inside apps, browsers, calendars, and spreadsheets. The real frontier is agents that can understand, coordinate, and act in the physical world. Iddris explains a vision for “agentic infrastructure for atoms”: systems that let AI agents manage physical objects, deliveries, storage, location, identity, logistics, robots, vehicles, and real-world context.

Robert Scoble

17,484 views • 3 months ago