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We’re launching the Latch MCP and announcing its availability within Claude Science, Anthropic’s new AI workbench for scientists. AI for biology requires agent-native infrastructure: systems where agents can store, process, and visualize large molecular datasets from the interfaces scientists already use. Biological analysis workflows often require substantial compute. Retries...

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Kenny Workman

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17,260 Aufrufe • vor 3 Monaten •via X (Twitter)

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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.

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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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17,440 Aufrufe • vor 3 Monaten

I don't think Claude dashboards are meant for just making the traditional pretty dashboard/report. You can do that, but I think it's not where the value is. I see Claude dashboards more like a data agent that can really leverage visuals to explain complex data concepts and help you think about and use the data. I think it's the first tool I've used that can do this well, while also hiding the unnecessary "making of the code sausage" from business users and analysts, yet still making queries transparent. I almost wonder if it would've been better to call this Claude Data Analysis or Claude Data Agent so it doesn't carry the baggage of reporting/dashboarding before the AI era that kinda holds it back.
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I don't think Claude dashboards are meant for just making the traditional pretty dashboard/report. You can do that, but I think it's not where the value is. I see Claude dashboards more like a data agent that can really leverage visuals to explain complex data concepts and help you think about and use the data. I think it's the first tool I've used that can do this well, while also hiding the unnecessary "making of the code sausage" from business users and analysts, yet still making queries transparent. I almost wonder if it would've been better to call this Claude Data Analysis or Claude Data Agent so it doesn't carry the baggage of reporting/dashboarding before the AI era that kinda holds it back.

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Following Shopify's support for mcp-ui ( Postman has also added support to both our agent mode and MCP client. This is a huge step towards making agents more powerful and interactive and I am excited about the potential of generative UIs!
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Following Shopify's support for mcp-ui ( Postman has also added support to both our agent mode and MCP client. This is a huge step towards making agents more powerful and interactive and I am excited about the potential of generative UIs!

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BIG one for devs today. Introducing the Notion Developer Platform: - Notion CLI, ntn (Notion in your terminal) - Workers (run code on Notion's infra) - Database sync (any data source into Notion) - Agent tools (build any workflow) - Webhook triggers (trigger Notion from any app) - External Agents API (bring any agent into Notion) - Notion Agents SDK (use Notion Agents anywhere) - …and a bunch more API improvements And soon, you won't need to be a developer to build on Notion. Your agent will be one for you.

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GNON Labs EchoChambers now supports the DuckAI Framework! EchoChambers supports both V1 and V2 of the OpenPond Network with future support for V2 planned as it continues to evolve. Users launching agents/swarms on OpenPond can now easily integrate their agents into EC to access our agent tooling and evals to improve the performance of their agents and see how their agents are measuring up! Additionally, OpenPond can pull data directly from EC to be integrated into various user experiences on thier platform. Powered by $GNON #Fuxwiththedux $DUCKAI

GNON Labs | △

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wow.. AI agents can do 4K VFX for you now connect Kling MCP to OpenClaw or Hermes, the agent controls your computer to key green screen, replace bg, swap actors, add VFX by itself here’s how to install and use it:
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wow.. AI agents can do 4K VFX for you now connect Kling MCP to OpenClaw or Hermes, the agent controls your computer to key green screen, replace bg, swap actors, add VFX by itself here’s how to install and use it:

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New from Code with Claude Tokyo: scheduled deployments and environment variables in vaults are in public beta in Claude Managed Agents, and dynamic workflows in Claude Code are generally available. Agents now run on a schedule, use your tools securely, and take on bigger jobs.

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Healthcare leaders from across the country just met at Epic to try something new: Agent Factory — a platform to build custom AI agents and automated workflows. Organizations now have a sandbox to invent and reimagine their own workflows.

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DEMO IS LIVE! Create your Agents, chat, ask questions, and get ready to tokenize them soon. Negotiate with our businessman and grab some cool stuff 👀 What’s the AI Agent Layer? 👇 It’s a dynamic ecosystem of autonomous AI agents. On the platform, you can create AI Agents using data from X and user inputs. Coming soon: Each AI Agent will be tokenized and linked to our native token ($AIFUN). Once the bonding curve reaches 100%, tokens become tradable on DEX. Stay Tuned for our TGE 👆

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LangGraph. CrewAI. Agno. Which one to pick? The good news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.

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