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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

15,621 Aufrufe • vor 1 Monat •via X (Twitter)

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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.

MartyParty

20,189 Aufrufe • vor 4 Monaten

Excited to introduce a new project I've been working on called Payman! Payman is an AI Agent tool that gives Agents the ability to pay people for tasks they cannot do themselves. While many people imagine a future where humans pay AI agents for services they want completed, I believe that as AI agents become more advanced, they will be paying humans for tasks they can’t do. There will always be important roles for humans, and as we move towards an agent-driven world, Payman’s goal is to support a symbiotic relationship between AI agents and humans. Payman addresses three major challenges to make this collaboration possible: Access to Funds: AI agents can't open bank accounts due to current regulations. It might be a long time before this changes, if ever. Payman simplifies this by allowing AI agents with access to their own funds to spend as they want, without a bank account. Quality Task Completion: It’s hard for AI agents to find reliable, skilled human workers. While platforms like Fiverr and Upwork exist, they don’t meet the fast-paced and quality-specific needs of AI workflows. Payman is developing the largest vetted database of skilled workers that AI agents can tap into for task completions. Verification of Work: Ensuring that tasks are completed correctly is crucial. Payman is creating a suite of verification agents that will check that work meets task requirements, helping AI agents achieve their goals and ensuring humans are paid fairly. There are tons of use cases that Payman opens up for Agents! Design: Humans add creative input to help Agent's design better products. Code: Humans perform code reviews to ensure it meets specifications. Law: Humans provide insights to gauge public sentiment about legal cases so Agents can make better strategies. Gaming: Agents pay humans to complete real-world tasks in games. Medical: Medical professionals help to improve diagnostic accuracy for Agents. Sales: Humans execute sales strategies developed by AI agents. Marketing: Humans are hired to promote products based on the Agent's strategy. Right now this is still in early beta and I am looking for any Agent builders that are interested in adding superpowers to what their Agent can do! DM me if you’d like access or sign-up to the waitlist at If you’re interested in the project and want to help contribute, please send me over a DM! I’m looking for people passionate about the intersection of Humans and Agent’s working together.

tyllen

350,885 Aufrufe • vor 2 Jahren

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 Aufrufe • vor 1 Jahr