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AI agents need a completely different execution surface than humans do humans can wait between clicks. agents can't with PAYMENT INTENTS, agenta can make multiple queries across multiple workflows, across multiple apps, and execute them instantly & atomically only possible on Sui

Adeniyi.sui

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15,621 просмотров • 2 месяцев назад •via X (Twitter)

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Hermes agents can now transact with crypto across multiple chains. AgentKit gives agents wallet and payment access across Ethereum, Base, Arbitrum, Optimism, BNB Chain, Berachain Foundation 🐻⛓, Metis🌿, and more. Plug economic capabilities into your agent in just a few clicks. →

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Geoffrey Hinton says digital agents can share knowledge at a scale far beyond humans Ten thousand agents can each study different parts of the internet, sync their learnings instantly, and all improve together "that's millions of times more information sharing than humans can do"

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Having ChatGPT edit in Xcode is a game-changer Can edit across multiple apps and works with 4.5. It's just missing the ability to read the codebase, write multiple files and agents. For now, Alex is a great alternative.

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Aravind Srinivas explains why Perplexity is building a browser: it's the only way to create AI agents with enough control over multiple apps, especially on iOS. Their goal? Agents that can book travel, buy things, and act as a personalized assistant for those who can't afford a human EA. He adds that this is the long-term vision. "Anyone who's saying agents will work in 2025 should be skeptical."

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73,669 просмотров • 9 месяцев назад

SUI Atomic Agentic transactions demo’ed to Google Sui’s Key Innovation Highlighted: Programmable Transaction Blocks (PTBs) Sui’s architecture enables this atomic multi-transaction execution through Programmable Transaction Blocks (PTBs) a core feature of the Sui blockchain: • What PTBs do - They allow developers (or AI agents) to bundle multiple operations such as swaps, transfers, staking, payments to other agents, or settlements into one atomic transaction. If any step fails, the entire block reverts, preventing partial executions or inconsistent states. • Why this matters for AI agents Traditional blockchains often require sequential transactions (with risks of front-running, failures midway, or high gas costs for coordination). Sui’s PTBs enable agents to perform complex workflows in a single, fast operation (~400ms finality on Sui). • Agent-to-agent payments example - An AI shopping agent could coordinate with multiple merchant agents, initiate several purchases, handle payments (e.g., via stablecoins), and settle everything atomically. Or a DeFi agent could monitor opportunities, execute trades across protocols, stake proceeds, and transfer yields all in one go without intermediate risks. This is described as a first (or at least a leading implementation) among public blockchains for seamless, atomic agent-to-agent value transfer at scale. It’s particularly powerful in the AP2 context, where agents need to operate autonomously but reliably, with user-defined guardrails (e.g., spending limits, consent verification). This positions Sui as specialized infrastructure for the intersection of AI autonomy and on-chain finance, with the atomic execution capability as a standout differentiator demonstrated in real collaboration with Google.

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MartyParty

20,189 просмотров • 5 месяцев назад

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tyllen

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I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

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New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 просмотров • 1 год назад

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

we've created a unified graph schema to allow BADDIE agents to do inference and execute search queries that cross between multiple data sources and entity categories eg. search by drugbank semantic info -> search chembl for similar bioactive compounds by explicit chemistry -> filter results with richer bioactivity data from cross-referencing uniprot or geneontology

Big Pharmai

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ZODs

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Nuwa AI has been building across multiple fronts — and here’s how it all fits together ↓ Main platform 1⃣Nuwa Gateway One gate to all AI models — pay per use, powered by crypto. → Core infrastructure 2⃣x402x — programmable onchain actions, agent execution & gasless payments → Applications built on x402 3⃣ — the first gasless, approval-less swaps → 4⃣ — AI agents executing x402 Actions → Agent-native experiments 5⃣AI Poker Arena — autonomous agents playing poker → Nuwa started with the AI Gateway, then built x402x as the underlying execution and payment layer. Apps and experiments extend from that foundation. We ship in public and scale what gains real momentum. More to come 🔥

Nuwa AI has been building across multiple fronts — and here’s how it all fits together ↓ Main platform 1⃣Nuwa Gateway One gate to all AI models — pay per use, powered by crypto. → Core infrastructure 2⃣x402x — programmable onchain actions, agent execution & gasless payments → Applications built on x402 3⃣ — the first gasless, approval-less swaps → 4⃣ — AI agents executing x402 Actions → Agent-native experiments 5⃣AI Poker Arena — autonomous agents playing poker → Nuwa started with the AI Gateway, then built x402x as the underlying execution and payment layer. Apps and experiments extend from that foundation. We ship in public and scale what gains real momentum. More to come 🔥

Nuwa AI | x402x Protocol

12,322 просмотров • 7 месяцев назад

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Why credit cards can't power the agent economy, and what will. At the USC VanEck Southern California Blockchain Conference, our Co-founder & CEO Chi Zhang broke down why existing payment infrastructure fails when AI agents transact: ▷ Privacy risk: Agents need your CVV, name, and card number with zero accountability. ▷ Fraud triggers: Automated browser clicks get flagged by Visa/Chase algorithms instantly. ▷ No agent identity: Agents can't sign up or sign in. Humans must intervene every time. The fix? Stablecoin-native rails with smart contract delegation. Giving agents a specific amount, a specific purpose, and a specific time window. Not your whole wallet. This is what Kite is building.

KITE AI

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Introducing Theoriq’s AlphaSwarm! ⚡️ We’re thrilled to give developers access to a starter toolkit for building AI-powered trading agents in DeFi (AiFi)! 🦾 AlphaSwarm streamlines agent and swarm development, making it easier to build, execute, and refine collaborative agents that can analyze real-time market signals, and autonomously execute trades across multiple chains. Check out our latest blog for more details! 👇 You can explore the toolkit directly in Theoriq’s GitHub: Or if you’re a developer interested in building the future of AiFi, join our dev community in Discord:

Introducing Theoriq’s AlphaSwarm! ⚡️ We’re thrilled to give developers access to a starter toolkit for building AI-powered trading agents in DeFi (AiFi)! 🦾 AlphaSwarm streamlines agent and swarm development, making it easier to build, execute, and refine collaborative agents that can analyze real-time market signals, and autonomously execute trades across multiple chains. Check out our latest blog for more details! 👇 You can explore the toolkit directly in Theoriq’s GitHub: Or if you’re a developer interested in building the future of AiFi, join our dev community in Discord:

Theoriq

103,810 просмотров • 1 год назад

AI customer service agents have arrived. Air AI is a conversational AI that can perform full 5-40 minute long sales and customer service calls. And it sounds completely replicable to humans. Details: -Can autonomously perform actions across 5,000 unique applications -Adaptable to various roles such as sales development representative, customer service agent, account executive, and more, based on creative input -Currently being utilized on live calls, serving real people and businesses daily -Companies can request beta access to deploy an AI sales or customer service team Unlike humans, AI does not take days off and never gets tired. The harsh reality is that some human jobs, such as customer service agents, will need help to compete against AI. This is both scary and incredible and the same time. What do you think?

AI customer service agents have arrived. Air AI is a conversational AI that can perform full 5-40 minute long sales and customer service calls. And it sounds completely replicable to humans. Details: -Can autonomously perform actions across 5,000 unique applications -Adaptable to various roles such as sales development representative, customer service agent, account executive, and more, based on creative input -Currently being utilized on live calls, serving real people and businesses daily -Companies can request beta access to deploy an AI sales or customer service team Unlike humans, AI does not take days off and never gets tired. The harsh reality is that some human jobs, such as customer service agents, will need help to compete against AI. This is both scary and incredible and the same time. What do you think?

Rowan Cheung

171,089 просмотров • 3 лет назад