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With the launch of GLM-5, introduces Agent Mode. - Agent Mode: Automatically breaks down tasks, orchestrates tools, drives execution, and delivers ready-to-use files. - Data Insights & Smart Writing: Upload data for instant visualizations. Go from outline to finished draft, all in one place. - AI Slides / Full-Stack...

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29,781 просмотров • 5 месяцев назад •via X (Twitter)

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The future of automation at the Department of War 🇺🇸 has arrived. We are excited to announce the launch of Agent Designer, now available in first AI instance, Google Google Gemini. All 3 million Department of War personnel now have the power to create their own custom AI assistants to automate tasks and streamline complex workflows, with absolutely no coding experience needed. These custom agents can perform multi-step tasks, ingest various data sources, and be shared with teams for immediate deployment. Now, operators can automatically generate after-action reports, analysts can synthesize CUI images into memos, and comptrollers can build apps to analyze financial data. From logistics to strategy, Agent Designer empowers you to build the tools needed for increased efficiency. Several pre-built agents are already available in Gemini to address common workflows. An “AI-first” Department of War empowers the warfighter to address bigger problems, rather than being burdened by tedious tasks.
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The future of automation at the Department of War 🇺🇸 has arrived. We are excited to announce the launch of Agent Designer, now available in first AI instance, Google Google Gemini. All 3 million Department of War personnel now have the power to create their own custom AI assistants to automate tasks and streamline complex workflows, with absolutely no coding experience needed. These custom agents can perform multi-step tasks, ingest various data sources, and be shared with teams for immediate deployment. Now, operators can automatically generate after-action reports, analysts can synthesize CUI images into memos, and comptrollers can build apps to analyze financial data. From logistics to strategy, Agent Designer empowers you to build the tools needed for increased efficiency. Several pre-built agents are already available in Gemini to address common workflows. An “AI-first” Department of War empowers the warfighter to address bigger problems, rather than being burdened by tedious tasks.

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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!
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Sharing our latest short course: Building and Evaluating Data Agents, created in collaboration with Snowflake and taught by Anupam Datta (Anupam Datta) and Josh Reini (Josh Reini). A data agent extracts data from sources such as files or databases, analyzes it, and provides insights and visualizes its findings. But most data agents struggle with reliability or can't handle multi-step reasoning. In this course, you'll learn to build, trace, and evaluate a multi-agent workflow that plans tasks, pulls context from structured and unstructured data, performs web search, and summarizes or visualizes the final results. Learn more and enroll for free!

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Data teams spend weeks on simple requests. (This AI answers them in minutes.) Most data analysis is repetitive manual tasks. Data teams spend more time on setup than actual analysis. The workflow usually looks like this: → Run some exploratory data analysis in a local Jupyter notebook or environment → Pull data from multiple disconnected sources → Write code from scratch for every analysis → Export static charts that stakeholders can't explore (or wrestle with legacy BI to create a dashboard) → Manually send updates via email or Slack when data changes → Start over for each new request Most teams accept this as "how data analysis works." While business decisions wait for insights. That's where Fabi changes the entire approach. It's a powerful, AI-native platform built for teams that want to boost productivity and supercharge their data workflows. Instead of working on separate tools and manual processes, you collaborate on analysis that automatically delivers insights where teams work. Here's what makes Fabi different: AI-Native Analysis Environment ↳ SQL and Python work together with AI assistance that handles coding and debugging automatically. Smart Automation Workflows ↳ Automatically send AI-powered reports and summaries right where business works in Slack, email, and spreadsheets. Universal Data Integration ↳ Analyze data from files, Google Sheets, Airtable, plus your data warehouse and databases in one place. Collaborative Data Apps ↳ Create interactive dashboards that stakeholders can explore and ask follow-up questions directly. What you can do with Fabi that legacy BI can't: ➟ Send AI-generated insights directly to Slack channels ➟ Automatically email data summaries to stakeholders ➟ Analyze uploaded files without complex ETL processes ➟ Collaborate on analysis like Google Docs for data ➟ Build workflows that push insights to spreadsheets Perfect for teams that want to move beyond the constraints of legacy and increase their impact. Teams using Fabi see immediate results: ✓ Insights delivered in minutes instead of days ✓ Reduced context switching between tools ✓ Stakeholders explore data independently ✓ Workflows automated to save hours of manual work From analysis to automated delivery - all in one AI-native environment. 📌 Try Fabi today: 👉 Follow Fabi.ai and marc for Fabi updates. 🔄 Repost to help other teams streamline data analysis #DataAnalysis #ModernBI #DataOps #InteractiveDashboards #FabiPartnership #SponsoredByFabi

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🚨 AI Agents Can Now Embed Any App And Create Complex Visualizations Weekend launches are the best!! - AI agents can embed and use any app - do detailed system and engineering design - create 3D models for data-centers and scientific research We cleverly mix and match LLMs. Opus 4.8 combined with GLM 5.2 and GPT 5..5 Here is a video of the Abacus AI agent designing a 3D data center

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Agent mode is now available in public preview for all users in Visual Studio 17.14. Agent mode in Visual Studio allows you to define tasks using natural language, with Copilot autonomously planning, editing your codebase, invoking tools, and iterating to resolve issues. Unlike Copilot Chat or Edits, agent mode doesn’t stop at one suggestion or file edit. Read more about it in our blog. 📖 #visualstudionews #githubcopilot #agent

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New course in collaboration with CrewAI, and taught by its Co-Founder and CEO, João Moura! In "Design, Develop, and Deploy Multi-Agent Systems" you'll go deep into building teams of AI agents that collaborate to handle complex, end-to-end workflows. You’ll design systems that plan, reason, and coordinate, with tools, memory, and guardrails that make them reliable and production-ready. Learn more and enroll now 👉 Featuring insights from Weaviate AI Database, Snyk, Exa, and AB InBev, whose work with CrewAI demonstrates how multi-agent systems are being applied in the field today.

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Grok 4.3 Beta is insanely good and now comes with powerful new tools that are super helpful for everyday use: • Create clean slides, graphs, images and dashboards • Pull real-time data and images directly from the web • Run smooth multi-step workflows • Analyze videos Grok is being built for real-world use - better tools, richer context, and real day-to-day signals make this a big step toward Grok becoming part of everyday life You can now research, visualize, and package your ideas end-to-end..... from concept to finished presentation or dashboard all in one place And the Grok Computer is coming very soon 🔥

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New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:
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New course: Building Coding Agents with Tool Execution, taught by Tereza Tizkova and Fra from E2B. Most AI agents are limited to predefined function calls. This short course teaches you to build agents that write and execute code to accomplish tasks, accessing entire programming language ecosystems instead of being restricted to a fixed set of tools. You'll learn to run agent-generated code safely in sandboxed cloud environments that protect your systems from harmful operations. Skills you'll gain: - Build agents that write and execute code, manage files, and handle errors autonomously through feedback loops - Run agent code safely in E2B cloud sandboxes and understand tradeoffs between local, containerized, and cloud execution - Create a data analyst agent that explores visualizes data with Pandas - Create a full-stack agent that builds complete Next.js web applications Join and build agents that code their way through complex tasks:

Andrew Ng

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Solidus Ai Tech Presents Agent Forge: Transforming AI Technology! Agent Forge is more than a platform—it's a revolution in how users interact with AI agents. Accessible to everyone, from seasoned professionals to newcomers, it empowers users to: 🔹Create Autonomous AI Agents: Automate processes, execute strategies, and stay ahead 24/7. 🔹 Gain Real-Time Insights: Stay informed with market updates via leading data platforms like CoinGecko and CoinMarketCap. 🔹 Simplify Complex Systems: Build automated workflows and manage digital strategies effortlessly, with no coding required. 🔹 Optimize Financial Management: Track data, optimize resources, and manage workflows with ease. 🔹 Breaking Barriers in AI Automation: Agent Forge makes advanced AI accessible to all, eliminating traditional technical challenges. 💡 Powered by AITECH HPC Data Center, Agent Forge ensures speed, security, and eco-friendly operations. 🗓 Coming Soon Agent Forge isn’t just a launch—it’s a leap forward.
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Solidus Ai Tech Presents Agent Forge: Transforming AI Technology! Agent Forge is more than a platform—it's a revolution in how users interact with AI agents. Accessible to everyone, from seasoned professionals to newcomers, it empowers users to: 🔹Create Autonomous AI Agents: Automate processes, execute strategies, and stay ahead 24/7. 🔹 Gain Real-Time Insights: Stay informed with market updates via leading data platforms like CoinGecko and CoinMarketCap. 🔹 Simplify Complex Systems: Build automated workflows and manage digital strategies effortlessly, with no coding required. 🔹 Optimize Financial Management: Track data, optimize resources, and manage workflows with ease. 🔹 Breaking Barriers in AI Automation: Agent Forge makes advanced AI accessible to all, eliminating traditional technical challenges. 💡 Powered by AITECH HPC Data Center, Agent Forge ensures speed, security, and eco-friendly operations. 🗓 Coming Soon Agent Forge isn’t just a launch—it’s a leap forward.

AITECH CLOUD NETWORK

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3. Agent factory: Foundry is the complete app platform for building apps and agents. We are adding support for more models from Grok, Hugging Face, Meta, Mistral, and more. Plus: Agentic retrieval in Azure AI Search, Foundry Agent Service, integration with Copilot Studio, and more. And we are ensuring the tools you already use for identity, management, and security will now all extend to agents too. The NFL has already used Foundry, along with our data stack, to run its recent Combine.
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3. Agent factory: Foundry is the complete app platform for building apps and agents. We are adding support for more models from Grok, Hugging Face, Meta, Mistral, and more. Plus: Agentic retrieval in Azure AI Search, Foundry Agent Service, integration with Copilot Studio, and more. And we are ensuring the tools you already use for identity, management, and security will now all extend to agents too. The NFL has already used Foundry, along with our data stack, to run its recent Combine.

Satya Nadella

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Agentic Browser is now live! This feature allows your AI agent to access real-time internet information for tasks like answering questions or finding recommendations. With this update, you can now see your AI agent's thought process before it responds to your questions—offering greater transparency and insights. Additional updates: •The initial quota of 50 messages per agent has been significantly increased to enable more extensive experimentation •We’ve fine-tuned the AI agents, making their responses more engaging and interactive •We also pushed along crypto social insights tools sourced from Coindesk, Cointelegraph, Cryptocompare and many more, your AI agent now have access to real-time market data
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Agentic Browser is now live! This feature allows your AI agent to access real-time internet information for tasks like answering questions or finding recommendations. With this update, you can now see your AI agent's thought process before it responds to your questions—offering greater transparency and insights. Additional updates: •The initial quota of 50 messages per agent has been significantly increased to enable more extensive experimentation •We’ve fine-tuned the AI agents, making their responses more engaging and interactive •We also pushed along crypto social insights tools sourced from Coindesk, Cointelegraph, Cryptocompare and many more, your AI agent now have access to real-time market data

ai9000

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THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.
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THIS GUY BUILT A COMMAND CENTER FOR MANAGING AI AGENT TEAMS he was running 20+ agents through openclaw. all from terminal tabs and Discord. 12 tabs open. still losing track of who's doing what. which cron failed. so he built a dashboard that gives you one place to see everything: > org map showing the full agent hierarchy, who reports to who and what tools each agent has > chat with any agent through a real UI instead of bouncing between terminals > kanban board for tracking tasks across all agents > cron monitor with pipeline visualizations so you can see which jobs are failing > memory browser to read through all agent knowledge files in one place you can even point it at your openclaw workspace and it auto-discovers every agent from their config files. zero setup. AND ITS OPEN SOURCE TOO.

Om Patel

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Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
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Introducing NEO: The first Agentic Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on the MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

19,571,775 просмотров • 11 месяцев назад

We're excited to introduce Box + Perplexity Computer – where your AI assistant becomes a true operator, working directly within your content hub. Empower your agents with: → A Real Filesystem for AI: Seamlessly connect Perplexity with your Box files → Intelligent Insights: Point Perplexity across multiple files to surface insights, patterns, and answers that drive strategic decisions. → Automated Multi-Step Workflows: Perplexity Computer handles complex tasks efficiently in the background. → Secure & Governed Operations: Leverage Box's security and permissions, ensuring your AI operates with the right access and governance. Watch how Box + Perplexity Computer transforms raw data into actionable intelligence, like building a comprehensive competitive brief from multiple documents.
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We're excited to introduce Box + Perplexity Computer – where your AI assistant becomes a true operator, working directly within your content hub. Empower your agents with: → A Real Filesystem for AI: Seamlessly connect Perplexity with your Box files → Intelligent Insights: Point Perplexity across multiple files to surface insights, patterns, and answers that drive strategic decisions. → Automated Multi-Step Workflows: Perplexity Computer handles complex tasks efficiently in the background. → Secure & Governed Operations: Leverage Box's security and permissions, ensuring your AI operates with the right access and governance. Watch how Box + Perplexity Computer transforms raw data into actionable intelligence, like building a comprehensive competitive brief from multiple documents.

Box

246,743 просмотров • 4 месяцев назад

Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:
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Introducing NEO: The first Autonomous Machine Learning Engineer. It works like a full-stack ML engineer that never sleeps: handling data exploration, feature engineering, training, tuning, deployment, and monitoring, end to end. Powered by 11 specialized agents, NEO runs autonomously, saving ML engineers thousands of hours and making them 10x faster. Benchmarks: Tested on 75 Kaggle competitions, NEO scored a medal in 34.2% of them, significantly outperforming Microsoft’s RD Agent (22.4%) on OpenAI's MLE Bench. This sets a new state of the art for autonomous ML systems. NEO runs on a novel multi-agent orchestrator, powered by a multi-step reasoning engine, context transfer protocol, and agent memory, built to solve complex workflows end to end. And with human-in-the-loop mode, you can guide, inspect, and override any step. You're always in command. NEO is built for real world workflows and ready for production. NEO is here to make every ML engineer truly superhuman. Watch NEO in action:

Neo AI

859,951 просмотров • 11 месяцев назад

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 👇
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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 просмотров • 1 год назад