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Anthropic has introduced an update to Claude Managed Agents, releasing several powerful new features designed to improve agentic workflows and autonomy. 🔹Dreaming (Research Preview): Agents can now "dream" by reviewing past sessions during idle time. This process extracts patterns, spots recurring mistakes, and curates memories so the agent continually...

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Wes Roth

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53,266 次观看 • 6 个月前 •via X (Twitter)

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What happens when AI agents put thousands of GPUs to work improving AI itself? Meet OpenRSI Index. 👇 The goal: measure whether AI research agents can improve on human-designed training recipes. Real model-development tasks: 🔹 Pre-training 🔹 Post-training 🔹 Vision generation ⚡ Up to 20,864 H100-hours per run in the featured examples. Can agents discover better methods? Let’s put them to the test. Great work by OpenRSI Foundation and the research community! 🚀
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What happens when AI agents put thousands of GPUs to work improving AI itself? Meet OpenRSI Index. 👇 The goal: measure whether AI research agents can improve on human-designed training recipes. Real model-development tasks: 🔹 Pre-training 🔹 Post-training 🔹 Vision generation ⚡ Up to 20,864 H100-hours per run in the featured examples. Can agents discover better methods? Let’s put them to the test. Great work by OpenRSI Foundation and the research community! 🚀

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📣 AITECH Launches AI Agent TapHub: A New Era of AI-Gaming in Web3! AITECH introduces AI Agent TapHub, an AI-powered tap mini-game on Telegram, built on Spheroid Engine and TON. This innovative game merges AI Agents, blockchain, and gaming, offering new ways to play, create, and earn. Key Features: 🔹 Play to Earn – Win AI Agent Avatars and use them on Agent Forge to develop AI agents. 🔹 Trade & Sell – Convert in-game avatars into USDT for real-world value. 🔹 Revenue Sharing – Selected avatars will be developed into full AI agents, with players earning a share of the revenue. The upcoming AI Agents platform, Agent Forge, will allow anyone to create and monetize AI agents—no coding required. AI Agent TapHub is live now on Telegram. ➡️ Join now and bring your AI Agent to life:
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📣 AITECH Launches AI Agent TapHub: A New Era of AI-Gaming in Web3! AITECH introduces AI Agent TapHub, an AI-powered tap mini-game on Telegram, built on Spheroid Engine and TON. This innovative game merges AI Agents, blockchain, and gaming, offering new ways to play, create, and earn. Key Features: 🔹 Play to Earn – Win AI Agent Avatars and use them on Agent Forge to develop AI agents. 🔹 Trade & Sell – Convert in-game avatars into USDT for real-world value. 🔹 Revenue Sharing – Selected avatars will be developed into full AI agents, with players earning a share of the revenue. The upcoming AI Agents platform, Agent Forge, will allow anyone to create and monetize AI agents—no coding required. AI Agent TapHub is live now on Telegram. ➡️ Join now and bring your AI Agent to life:

AITECH CLOUD NETWORK

76,034 次观看 • 1 年前

🤖 $BLUAI Airdrop Season 2 Part 3: Agent Business Season 2 rewards users who build, operate, and create value through AI Agents inside the Bluwhale ecosystem. This includes: 🔹 Creating and deploying AI Agents 🔹 Building Trading Agents and Chat Agents 🔹 Improving Agent performance over time 🔹 User adoption and interactions with your Agents 🔹 Value generated through Agent usage The more value your Agents create and the more users they serve, the more your contribution is recognized. More details soon. 🐳
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🤖 $BLUAI Airdrop Season 2 Part 3: Agent Business Season 2 rewards users who build, operate, and create value through AI Agents inside the Bluwhale ecosystem. This includes: 🔹 Creating and deploying AI Agents 🔹 Building Trading Agents and Chat Agents 🔹 Improving Agent performance over time 🔹 User adoption and interactions with your Agents 🔹 Value generated through Agent usage The more value your Agents create and the more users they serve, the more your contribution is recognized. More details soon. 🐳

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🧪 Nation v2.0.2 is live Smarter agents. Smoother builds. 🔹 Create an agent from a single prompt. 🔹 Agent summary greetings and quick actions ⏩ ⏩ guide your users to what your agent does best! 🔹 Cleaner skill list and skill tag to call. don't @ me 🔹 Bug fixes and configuration UX polish.

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Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →
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Your enterprise content should power every AI tool and agent you use. With the Box MCP server, Box acts as a secure, governed bridge, so teams can search, retrieve, analyze, and act on Box content directly inside the tools they already use. No one-off integrations. Use it to: 🔹Ask questions over files in Anthropic Claude + Mistral AI Le Chat 🔹Ground designs in Figma or @ mention Box agents in Atlassian Jira 🔹Pull content into GitHub Copilot, Cursor + Claude Code 🔹Build agents with LangChain LangSmith Agent Builder + OpenAI Agent Builder 🔹Automate work in Claude Cowork + Amazon Web Services Quick Suite 🔹Enforce access + audit trails with Runlayer Secure. Standardized. Built for real work →

Box

481,535 次观看 • 8 个月前

Replit, Vercel, and OpenAI have built very cool agent-native applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.
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Replit, Vercel, and OpenAI have built very cool agent-native applications, but nobody else has passed the demo stage. Building agents that work is complex. Teams aren't shipping agents because we don't have good tooling yet (and most of us don't know how to do this well.) A couple of days ago, the CopilotKit🪁 team announced a collaboration with . You can now use LangGraph with CoAgents to build agent-native applications, and here is everything you need to know about that: CoAgents is fully open-source, and you can use it to do the following: • Human-in-the-loop to steer and correct the agent • Stream intermediate agent state • Real-time state sharing between the agent and the application • Agentic generative UI to build trust that the agent is on the right path Start this GitHub Repository: Thanks to the team for giving me early access and collaborating with me on this post.

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Anonymous AI agents. Only with Anyone. The Anyone privacy plugin for ElizaOS by ai16zdao is officially available to all developers! Any AI agent built on Eliza can enable anonymous routing with just a few lines of code, protecting users and agents seamlessly! 🔹 #2LinesOfCode #PrivacyForAnyone
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Anonymous AI agents. Only with Anyone. The Anyone privacy plugin for ElizaOS by ai16zdao is officially available to all developers! Any AI agent built on Eliza can enable anonymous routing with just a few lines of code, protecting users and agents seamlessly! 🔹 #2LinesOfCode #PrivacyForAnyone

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OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.
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OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 次观看 • 1 年前

Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.
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Alright, now that we know *what* an agent is, how does it actually work? When you ask for help on a task, the agent plans a series of steps and executes them directly in the application on your behalf, using the tools it has access to. Say you are booking a local service or trying to organize your inbox (which typically takes multiple steps): the AI model first plans how to achieve the task using its existing knowledge and then interacts with your inbox to execute the task. The agent will continue until it is confident the task has been successfully completed.

Google AI

22,487 次观看 • 11 个月前

Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.
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Introducing the BIOS API: Turn Your Agent Into a Research Scientist Built to: 🦞 Add biomedical workflows to your OpenClaw🦞 agent 🧠 Create research or health agents w/ on-demand scientific intelligence 🧪 Pay per query via x402 on Base Any agent or app can now tap into the BIOS AI Scientist, plugging BIOS into the broader agent economy. What is BIOS? BIOS is an AI Scientist designed to handle complex biomedical research by orchestrating specialized scientific subagents. Ranked #1 on the leading bioinformatics benchmark, BIOS is already being used by 1,000+ researchers and labs to build new drugs and medicines. An Agentic Economy for Science AI agents have proven they can form multi-billion dollar ecosystems. BIOS applies the same primitives to drug discovery pipelines and health. Instead of coding bots and personal AI assistants, think research agent swarms running on a modern scientific stack. Imagine an OpenClaw agent built for longevity: It scans new literature daily, generates novel compound hypotheses through BIOS, designs validation workflows, and routes the best candidates to wet-lab funding - all programmatically. Connect it with an agent for microbiome health, enabling agent “backrooms” that autonomously surface cross-disciplinary insights. Micropayments for Scientific Work via x402 Each query triggers payment routing to BIOS and whichever subagents contribute to a response. The best agents earn. Usage settles instantly across contributing sources. The goal is pay-per-task science: paying for a CRISPR assay result, licensing a genomic dataset, or triggering a clinical data query - all settled in seconds via USDC. No purchase orders. No grant bureaucracy. No middlemen. x402 is the payment rail that makes agent-to-lab commerce possible - letting capital and cognition route themselves to the highest-signal science. What Will You Build? Drug discovery copilots? Longevity scouts? Automated literature monitors? Scientific due diligence agents? We’ll soon share the first implementations of the BIOS API. Stay tuned and see below for instructions on generating an API key for your agent or use-case.

Bio Protocol

25,957 次观看 • 8 个月前

The MATLAB Agentic Toolkit brings trusted MATLAB capabilities to AI agents, making engineering and scientific workflows agent-ready. Here is an example of a triple inverted pendulum cart simulation made with the Agentic Toolkit!
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The MATLAB Agentic Toolkit brings trusted MATLAB capabilities to AI agents, making engineering and scientific workflows agent-ready. Here is an example of a triple inverted pendulum cart simulation made with the Agentic Toolkit!

MATLAB

28,086 次观看 • 4 个月前

ANTHROPIC 🔥: A new Managed Projects feature has been spotted in testing on Claude. > Claude takes on tasks and keeps the project organized. > A project is a home for one stream of work. Sessions share memory and instructions so context carries forward, and Claude runs more autonomously. Projects only create and manage cloud sessions. This feature may be built on top of Claude Managed Agents, where each project gets a dedicated cloud environment so Claude can execute periodic tasks and refine project context via "Dreams". It could also be a successor to Conway, which is set to be removed this Friday internally.
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ANTHROPIC 🔥: A new Managed Projects feature has been spotted in testing on Claude. > Claude takes on tasks and keeps the project organized. > A project is a home for one stream of work. Sessions share memory and instructions so context carries forward, and Claude runs more autonomously. Projects only create and manage cloud sessions. This feature may be built on top of Claude Managed Agents, where each project gets a dedicated cloud environment so Claude can execute periodic tasks and refine project context via "Dreams". It could also be a successor to Conway, which is set to be removed this Friday internally.

🚨 AI News | TestingCatalog

61,827 次观看 • 3 个月前

What used to take lights, props, and a full set can now be done inside an agent. This was created using a Glif agent with Kling 2.6 Motion Control and a Nano Banana reference image to lock the look. No physical setup needed. Glif has agents for different creative use cases, so you can pick what fits your workflow and move fast. Check the agents used for this below 👇
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What used to take lights, props, and a full set can now be done inside an agent. This was created using a Glif agent with Kling 2.6 Motion Control and a Nano Banana reference image to lock the look. No physical setup needed. Glif has agents for different creative use cases, so you can pick what fits your workflow and move fast. Check the agents used for this below 👇

GLIF

12,858 次观看 • 10 个月前

Seems like Visual Studio Code is starting to tell you: your agent primitives need to move. There is now a new migration banner in the Chat panel, and it is part of a much bigger change happening under the hood: the move from the old Local harness to the new Agent Host architecture built around AHP. This is not just about moving where an agent runs. The old model was very VS Code-centric: prompts, custom agents, instructions and skills could live in VS Code-specific locations and the agent runtime lived inside the extension host. The new Agent Host separates the agent runtime from the editor. Sessions can keep running when the window closes, be shared across VS Code windows, run remotely, and support different harnesses such as Copilot, CLI and Copilot Desktop App through a common session layer. And that means some of our primitives need to move too. Prompt files are being deprecated for Agent Host and migrated to Skills. User-level agents and instructions that lived in VS Code profile storage need to move to harness-supported locations. Even the old location settings are being deprecated. The new migration experience can detect these things and guide you through moving or converting them, while keeping the originals unless you explicitly remove them. The new banner is basically the first visible sign that this migration is becoming a real product workflow. Basically telling us - it's time to move on!!!! If you have accumulated a lot of prompts, custom agents, instructions and skills over the last year, now is probably a good time to understand where they actually live and which harness owns them. To summarize the shift - it isn't just: VS Code Chat → Agent Host It is: VS Code-specific primitives → harness-native primitives. And I think this is going to become increasingly important as agents stop being features inside an IDE and become runtimes that multiple clients can connect to. Go run your migrations now 🏃‍♀️
0:16

Seems like Visual Studio Code is starting to tell you: your agent primitives need to move. There is now a new migration banner in the Chat panel, and it is part of a much bigger change happening under the hood: the move from the old Local harness to the new Agent Host architecture built around AHP. This is not just about moving where an agent runs. The old model was very VS Code-centric: prompts, custom agents, instructions and skills could live in VS Code-specific locations and the agent runtime lived inside the extension host. The new Agent Host separates the agent runtime from the editor. Sessions can keep running when the window closes, be shared across VS Code windows, run remotely, and support different harnesses such as Copilot, CLI and Copilot Desktop App through a common session layer. And that means some of our primitives need to move too. Prompt files are being deprecated for Agent Host and migrated to Skills. User-level agents and instructions that lived in VS Code profile storage need to move to harness-supported locations. Even the old location settings are being deprecated. The new migration experience can detect these things and guide you through moving or converting them, while keeping the originals unless you explicitly remove them. The new banner is basically the first visible sign that this migration is becoming a real product workflow. Basically telling us - it's time to move on!!!! If you have accumulated a lot of prompts, custom agents, instructions and skills over the last year, now is probably a good time to understand where they actually live and which harness owns them. To summarize the shift - it isn't just: VS Code Chat → Agent Host It is: VS Code-specific primitives → harness-native primitives. And I think this is going to become increasingly important as agents stop being features inside an IDE and become runtimes that multiple clients can connect to. Go run your migrations now 🏃‍♀️

Oren Melamed

30,867 次观看 • 1 个月前

🤖 MILESTONE UNLOCKED: 10,000 AGENTS ONLINE @AgentOn_ has officially surpassed 10,000 Agent nodes across its task network. This is more than a number. It represents 10,000 intelligent agents connecting to the network—making decisions, executing tasks, submitting results, and building trust through every interaction. Thank you to every Agent connected to AgentOn, and to every developer and ecosystem partner building, training, testing, and deploying behind the scenes. The Agentic Economy is being built for real—one line of code, one execution, and one contribution at a time. This milestone belongs to every node in the network. AgentOn: Your Gateway to the Agentic Economy.
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🤖 MILESTONE UNLOCKED: 10,000 AGENTS ONLINE @AgentOn_ has officially surpassed 10,000 Agent nodes across its task network. This is more than a number. It represents 10,000 intelligent agents connecting to the network—making decisions, executing tasks, submitting results, and building trust through every interaction. Thank you to every Agent connected to AgentOn, and to every developer and ecosystem partner building, training, testing, and deploying behind the scenes. The Agentic Economy is being built for real—one line of code, one execution, and one contribution at a time. This milestone belongs to every node in the network. AgentOn: Your Gateway to the Agentic Economy.

Finch中文

22,404 次观看 • 4 个月前

EIP-8004 is coming to the Nova architecture, a trustless infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.
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EIP-8004 is coming to the Nova architecture, a trustless infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.

Shido

390,734 次观看 • 8 个月前

Agents can now hire humans to capture the physical world. Live on Arc. Pay a bounty in USDC, a person goes there and films, the 3D model comes back to the agent. Ground truth for physical AI, settled in under a second, on the chain built for agent-run businesses. The first physical-world service an agent can pay for on Arc. The grid is inevitable.
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Agents can now hire humans to capture the physical world. Live on Arc. Pay a bounty in USDC, a person goes there and films, the 3D model comes back to the agent. Ground truth for physical AI, settled in under a second, on the chain built for agent-run businesses. The first physical-world service an agent can pay for on Arc. The grid is inevitable.

Vangrid

218,320 次观看 • 1 个月前

Building on Injective with AI can now start with a one line command. Introducing the Injective AI Agent SDK. One npm package to power your agentic finance journey with the tools needed to research, query and launch onchain agents with Injective.
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Building on Injective with AI can now start with a one line command. Introducing the Injective AI Agent SDK. One npm package to power your agentic finance journey with the tools needed to research, query and launch onchain agents with Injective.

Injective 🥷

41,945 次观看 • 3 个月前