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It can be challenging for AI agents to solve complex user intents by synthesizing signals like screenshots, the DOM, and the Accessibility Tree. Enter WebMCP, a proposed web standard that aims to expose structured tools for AI agents directly on existing websites, now in origin trial ⬇️

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14,702 görüntüleme • 1 ay önce •via X (Twitter)

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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:
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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:

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Launching AI Discovery And Communication DeepAgent discovers other AI agents and software tools on the fly and communicates with them to perform complex tasks. Here is how it works - - DeepAgent searches for other AI agents and tools - Finds the right tool for the job - Establishes contact using MCP and handles auth - Collaborates with the other tool to get the job done! - Remembers this tool for future colabs! Much like humans, it can create a team of AI agents or software tools to perform tasks. Here it is, learning about MindMaps, communicating with another server, and creating a Mindmap!!

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We're excited to announce that Coinbase Developer Platform🛡️ AgentKit supports the new OpenAI Agents SDK. OpenAI’s new Agents SDK is an open source framework for building and scaling agents that includes built-in tools for web search, file search, and computer use, and tools to track and optimize agent performance—making it easier than ever to build production-ready AI agents. Coinbase AgentKit complements this by adding secure crypto wallets directly to your agents, enabling them to transact globally, instantly, and with near-zero fees—unlocking true financial autonomy.

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Our agents - and their actions - can now be insured. Backed by AIUC-1 certification, we’re the first company to secure insurance for AI voice agents.

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Aravind Srinivas's hypothesis on how advertising works with AI agents is quite interesting. Aravind explains, Instead of showing ads to humans directly, advertisements would be targeted at AI agents that work on users' behalf. Users never see ads, they simply tell their AI agents what they need for eg if he gave, "I'm going to go to Rambagh for two nights, plan my schedule, do my bookings" In the backend, Multiple vendors compete for the agent's attention & companies bid for the agent's consideration. So everything happens at the AI level. Agents can also use more compute power to analyse offer to find out advertising attempts & make decisions based on user preferences. Also users can instruct their agents to ignore certain brands , this will remain confidential to between the agent & the user. This creates new revenue streams for AI companies & gives a better user experience.

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Wow! Agents can now trade autonomously on Polymarket using the x402 standard. Semantic Layer is leading the way here, and I think it makes a lot of sense to have AI Agents executing trades directly on prediction markets, as they can analyze vast amounts of data and close inefficiencies. This first beta already features three specialized Agents, each researching a different Polymarket sector. AI agents are automating everything.

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We're giving you $1,500,000 to help us build the perfect economy. The Great Tournament of Agents is your ticket to knowledge, prizes, and fun on Sophon! Enter the GTA and we'll give you the tools to create cutting-edge AI agents that compete for PNL and Mindshare. Learn more by clicking the link in the reply.

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Shipping this feature to Superset is like scratching an itch I've had for months You can now annotate on any changes and send it directly to your agents! Works for any CLI agents. Spin up a new agent or send comment to your existing session 🔥 Powered by the wonderful Pierre diffs as always

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AI agents can read any file the user has access to. That is a much larger surface than most security teams realize. Classification-based access policies in Box now give admins read access control for AI agents. Restrict which agents can read which content based on Box Shield classifications. Enforced automatically across every agent, regardless of which AI tool is making the request. This demo shows it in action.

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Introducing Agent Blocks. AI agents can now work together to solve more complex problems. - Start with simple agents - Combine them into sophisticated solutions - Build on top of other's work Available now in the AutoGPT Platform ✅ Learn more ➡️

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6:35

🦞We’re proud to introduce DuckyClaw, a hardware-native AI agent framework now available to developers in the TuyaOpen ecosystem. With DuckyClaw, developers can deploy AI agents directly on embedded hardware, from T5 boards to Raspberry Pi, various MCUs and SoCs, enabling devices to sense, decide, and act in the physical world. Inspired by the rapid rise of OpenClaw, which showed how AI agents can automate tasks on computers, DuckyClaw takes the next step: bringing AI agents to hardware.

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Our vision for Simulacrum is to enable anyone to create onchain AI agents with a single post or DM 🪄 How? By allowing builders & protocols to add skills that can then be stacked like Lego pieces to make custom AI agents: - Uniswap - DEXScreener - Etherscan - X feed and much more The only limits? Builder imaginations and user creativity 🚀 👉 See how at
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Our vision for Simulacrum is to enable anyone to create onchain AI agents with a single post or DM 🪄 How? By allowing builders & protocols to add skills that can then be stacked like Lego pieces to make custom AI agents: - Uniswap - DEXScreener - Etherscan - X feed and much more The only limits? Builder imaginations and user creativity 🚀 👉 See how at

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