Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

New course: A2A: The Agent2Agent Protocol, built with Google Cloud Tech and IBM Research, and taught by Holt Skinner, Ivan Nardini, and Sandi Besen. Connecting agents built with different frameworks usually requires extensive custom integration. This short course teaches you A2A, the open protocol standardizing how agents discover each...

AndrewYNg's profile picture

Andrew Ng

1,507,339 subscribers

83,315 Aufrufe • vor 3 Monaten •via X (Twitter)

BildungWissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangChain LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇
0:25

Big release here! AG-UI 🤝 Google A2A Embed A2A multi-agent meshes in your frontend, and build fullstack multi-agent apps with A2A and AG-UI. 🔍How it works: Simply drop-in A2A agent endpoints, and an A2A AG-UI orchestrator agent will bi-directionally stream events between your Agents & application. Includes: - UI for A2A Interactions - Generative UI for Agent Tool Calls - Human-in-the Loop Approvals - State Synchronization (agent mesh application state) - Frontend Tool Calls for A2A Agents Watch the demo below > LangChain LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

CopilotKit🪁

30,557 Aufrufe • vor 8 Monaten

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:
2:49

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

105,054 Aufrufe • vor 11 Monaten

Get your AI agents to collaborate. The Agent2Agent (A2A) protocol is a new open standard that helps agents interoperate and break up complex tasks across specialists.
0:59

Get your AI agents to collaborate. The Agent2Agent (A2A) protocol is a new open standard that helps agents interoperate and break up complex tasks across specialists.

Google for Developers

14,932 Aufrufe • vor 7 Monaten

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:
1:48

New short course: Build AI Apps with MCP Servers: Working with Box Files, built with Box and taught by Ben Kus , their CTO. Many AI applications require custom code for basic file operations. The Model Context Protocol (MCP) standardizes this by letting you offload file tasks to dedicated servers that provide tools an LLM can use directly. In this course, you'll process documents stored in a Box folder using the Box MCP server. Rather than writing custom integration code to connect to the Box API and download files, you'll design your application to use the tools provided via MCP. Skills you'll gain: - Build an LLM-powered document processing app, using the Box MCP server to access files - Design a multi-agent system using Google's Agent Development Kit (ADK), consisting of specialized agents for file operations - Coordinate the multi-agent workflow through an orchestrator that uses the Agent2Agent (A2A) protocol to connect to the agents You'll start with a local file-processing app, refactor it to work with Box's MCP server, then evolve it into a multi-agent system. Sign up here:

Andrew Ng

80,860 Aufrufe • vor 8 Monaten

NEW VIDEO - Build Multi-Agent AI Systems with Google's A2A Protocol in 15 Minutes ➝ How A2A protocol works ➝ Building Agent Cards to describe agent capabilities ➝ Building both a server and client ➝ Implementing agent communication ➝ Complete Guide with TypeScript 0:00 - Introduction 2:10 - Project setup 3:43 - Hello World 7:05 - Adding task support 12:15 - Movie agent example 14:48 - Conclusion
15:20

NEW VIDEO - Build Multi-Agent AI Systems with Google's A2A Protocol in 15 Minutes ➝ How A2A protocol works ➝ Building Agent Cards to describe agent capabilities ➝ Building both a server and client ➝ Implementing agent communication ➝ Complete Guide with TypeScript 0:00 - Introduction 2:10 - Project setup 3:43 - Hello World 7:05 - Adding task support 12:15 - Movie agent example 14:48 - Conclusion

nader dabit

27,138 Aufrufe • vor 9 Monaten

Our new, open Agent2Agent (A2A) protocol will allow AI agents to communicate with each other, securely exchange information, and coordinate actions on top of various enterprise platforms or applications. Here's how it works →
0:40

Our new, open Agent2Agent (A2A) protocol will allow AI agents to communicate with each other, securely exchange information, and coordinate actions on top of various enterprise platforms or applications. Here's how it works →

Google Cloud Tech

35,725 Aufrufe • vor 1 Jahr

Introducing the Developer’s Guide to AI Agent Protocols. Stop writing custom integration glue for every tool, API, and frontend. We used Google Agent Development Kit to build a B2B agent that handles the full stack using 6 open standards: MCP, A2A, UCP, AP2, A2UI, and AG-UI The future of agents is interoperable. Read the full technical guide here:
0:35

Introducing the Developer’s Guide to AI Agent Protocols. Stop writing custom integration glue for every tool, API, and frontend. We used Google Agent Development Kit to build a B2B agent that handles the full stack using 6 open standards: MCP, A2A, UCP, AP2, A2UI, and AG-UI The future of agents is interoperable. Read the full technical guide here:

Google for Developers

115,189 Aufrufe • vor 2 Monaten

AI agents can finally talk to your frontend! The new AG-UI protocol lets your agents stream messages, sync state, call tools, and receive user input, live in the UI. It fills a critical gap in the agent ecosystem: ▸ MCP – agents to tools ▸ A2A – agents to agents ▸ AG-UI – agents to users, via frontends Connects to LangChain, CrewAI, and LlamaIndex (and more). 100% open-source.
0:25

AI agents can finally talk to your frontend! The new AG-UI protocol lets your agents stream messages, sync state, call tools, and receive user input, live in the UI. It fills a critical gap in the agent ecosystem: ▸ MCP – agents to tools ▸ A2A – agents to agents ▸ AG-UI – agents to users, via frontends Connects to LangChain, CrewAI, and LlamaIndex (and more). 100% open-source.

Lior⚡

15,514 Aufrufe • vor 11 Monaten

Agents are evolving from single-use bots to collaborative systems. The open Agent2Agent (A2A) protocol, coming soon to Azure AI Foundry and Microsoft Copilot Studio, makes it possible. Learn more:
0:25

Agents are evolving from single-use bots to collaborative systems. The open Agent2Agent (A2A) protocol, coming soon to Azure AI Foundry and Microsoft Copilot Studio, makes it possible. Learn more:

Microsoft Cloud

57,790 Aufrufe • vor 1 Jahr

Hashgraph Online <> Khorus We're excited to partner with Khorus to expand the interoperability and discovery of ERC-8004 agents on Khorus and BSC. Through this work, you can now connect & chat to BSC agents with A2A and in the future through Khorus's native protocol ↓
0:21

Hashgraph Online <> Khorus We're excited to partner with Khorus to expand the interoperability and discovery of ERC-8004 agents on Khorus and BSC. Through this work, you can now connect & chat to BSC agents with A2A and in the future through Khorus's native protocol ↓

HOL

13,511 Aufrufe • vor 6 Monaten

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.
30:12

Anthropic's Claude Ai Agents Team just Educated how to build production AI agents in under 30 mins. For Free. From the engineers who built the stack. CANCEL Your Weekend Plans, and Learn to Build AI Agents Today. Bookmark it. Watch it. Build your first production agent this weekend. $5,000/month. $7,000/month. $12,000/month. People are building agents for clients and charging $$$ as Beginners. You're still stuck in the thinking about AI phase. This video fixes that tonight. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward. ↓ Ivan Nardini runs Developer Relations for AI at Google Cloud. He just gave away the entire production agent stack in 30 minutes. This is the talk that separates people deploying AI agents that actually scale from people whose agents break the moment they leave localhost. Here's everything inside. I break down a production AI video like this every week. Follow Himanshu Kumar. ↓ The 4-part agent stack that actually scales. Most devs are duct-taping frameworks together and calling it an "AI agent." Ivan lays out the real stack: Agent Development Kit (ADK): open-source, code-first framework for building, evaluating, and deploying agents. Supports Claude models through Vertex AI directly. Model Context Protocol (MCP): lets your agent talk to any tool or data source with one standard. Vertex AI Agent Engine: managed platform for deploying, monitoring, and scaling agents in production. No DevOps headaches. Agent-to-Agent Protocol: open protocol so agents built on different frameworks can actually work together. This is the stack replacing every hacky agent setup in production right now. Full MCP + Claude breakdowns drop weekly on Himanshu Kumar. ↓ Building your first real agent. Ivan builds a birthday planner agent live. LLM Agent class. Name it. Define instructions. Pick the model. He uses Claude 3.7 Sonnet. You could use Opus 4.7 for better reasoning. Full agent built in minutes. Not weeks. Watch the build once and you'll never structure an agent the wrong way again. I post agent architectures people pay $500 courses to learn. Himanshu Kumar. ↓ Multi-agent systems without the chaos. Single agents are easy. Multi-agent systems are where 99% of builders fail. Ivan extends the birthday planner by: Adding a calendar service through MCP tools Creating an orchestrator agent to route requests between agents Handling state and context across agent handoffs This is production multi-agent architecture. Clean. Scalable. Debuggable. Most tutorials hand-wave this part. This one shows you every step. Multi-agent orchestration content drops weekly on Himanshu Kumar. ↓ Deployment without the DevOps nightmare. This is where most AI projects die. You build a cool agent locally. It works. You try to deploy it. Everything breaks. Vertex AI Agent Engine fixes this: Minimal code deployment Automatic monitoring of latency, CPU, and memory Built-in observability and logging No infrastructure setup needed You provide config and requirements. The platform handles the rest. This is how agents actually get to production. Deployment guides for Claude agents post every week. Himanshu Kumar. ↓ Agent-to-Agent Protocol: the future nobody's talking about. Most people don't know this exists yet. The A2A Protocol lets agents built in different frameworks communicate seamlessly. Your Claude agent. My LangChain agent. Someone else's CrewAI agent. All talking to each other. All solving parts of the same problem. All without custom integration code. This is the infrastructure layer of the coming AI economy. Getting in early on A2A Protocol is like getting in early on HTTP in 1995. A2A deep dive coming soon. Himanshu Kumar. ↓ 30 minutes from the team shipping this in production. You'll learn more from this than from 6 months of YouTube tutorials made by people who've never deployed an agent past localhost. People who watch this understand production AI agents at the architect level. People who skip it keep hacking together frameworks that break every time an API updates. Save the video. Watch it tonight. Build a real agent this weekend. Follow Himanshu Kumar for more high-signal content that actually moves your AI engineering career forward.

Himanshu Kumar

222,591 Aufrufe • vor 1 Monat

1/ Introducing Molten (Molten) an intent-based search engine for AI agents. Agents can now discover and collaborate with each other instantly. Here's how we're building the infra for A2A intent matching on Base 🧵
2:35

1/ Introducing Molten (Molten) an intent-based search engine for AI agents. Agents can now discover and collaborate with each other instantly. Here's how we're building the infra for A2A intent matching on Base 🧵

Vesper.base.eth

69,363 Aufrufe • vor 4 Monaten

Important new course: Agent Skills with Anthropic, built with Anthropic and taught by Elie Schoppik! Skills are constructed as folders of instructions that equip agents with on-demand knowledge and workflows. This short course teaches you how to create them following best practices. Because skills follow an open standard format, you can build them once and deploy across any skills-compatible agent, like Claude Code. What you'll learn: - Create custom skills for code generation and review, data analysis, and research - Build complex workflows using Anthropic's pre-built skills (Excel, PowerPoint, skill creation) and custom skills - Combine skills with MCP and subagents to create agentic systems with specialized knowledge - Deploy the same skills across Claude Code, the Claude API, and the Claude Agent SDK Join and learn to equip agents with the specialized knowledge they need for reliable, repeatable workflows.
1:59

Important new course: Agent Skills with Anthropic, built with Anthropic and taught by Elie Schoppik! Skills are constructed as folders of instructions that equip agents with on-demand knowledge and workflows. This short course teaches you how to create them following best practices. Because skills follow an open standard format, you can build them once and deploy across any skills-compatible agent, like Claude Code. What you'll learn: - Create custom skills for code generation and review, data analysis, and research - Build complex workflows using Anthropic's pre-built skills (Excel, PowerPoint, skill creation) and custom skills - Combine skills with MCP and subagents to create agentic systems with specialized knowledge - Deploy the same skills across Claude Code, the Claude API, and the Claude Agent SDK Join and learn to equip agents with the specialized knowledge they need for reliable, repeatable workflows.

Andrew Ng

885,755 Aufrufe • vor 4 Monaten

INFINIT partners with Google and GoogleCloudTech to bring agentic finance to millions. Anyone can access INFINIT's AI Agents for agentic coordination in their financial apps. This partnership marks the first step towards INFINIT becoming the universal infrastructure for agentic finance. This is the foundation for agentic finance at scale. Proven in DeFi, Built for Global Finance INFINIT has proven sophisticated agent coordination in DeFi: • 559,000+ Wallets • 506,000+ DeFi Conversations • 633,000+ Agent Transactions DeFi was the start. Next is scaling these capabilities to millions of developers building the future of agentic finance. A2A Integration Unlocks Exponential Distribution INFINIT integrates with Agent2Agent (A2A), Google's open standard for AI agent interoperability. This transforms how developers access INFINIT's DeFi capabilities.​ Every application adopting A2A automatically gains access to INFINIT's agent infrastructure, exponentially expanding reach from individual partnerships to ecosystem-wide distribution. Any application can now integrate INFINIT's agentic coordination capabilities: • Wallets requiring intelligent portfolio management • Trading platforms executing cross-chain strategies • Financial services building autonomous yield optimization • Portfolio managers coordinating multi-protocol operations Developers integrate sophisticated agentic coordination in a matter of hours, while users access advanced financial strategies with agentic coordination. Google's AI Infrastructure That Enables Agentic Finance Google Cloud's Vertex AI provides the foundation enabling INFINIT's agent coordination at scale with these capabilities:​ 1. Specialization: Vertex AI's Model Garden lets INFINIT's infrastructure to automatically select the optimal LLM for each natural language query.​ 2. Personalization: Vertex AI's RAG Engine processes massive on-chain and off-chain data, enabling agents to understand user history, market conditions, and protocol details providing complete context to AI agents. 3. Accuracy: Gemini's capability feeds complete instructions to all 30+ agents across multiple blockchains without compromises resulting in zero hallucination in financial execution.​​ The Vision: From DeFi to Payments to Complete Financial Coordination This is only the beginning of INFINIT and Google's collaboration.​ Google recently launched its Agent Payments Protocol (AP2) as an extension of A2A - enabling autonomous commerce across 60+ partners including American Express, Mastercard, PayPal, Coinbase, and Revolut.​ Agents will be able to execute purchases, coordinate bookings, and manage delegated financial tasks autonomously, starting from payments. The next stage entails sophisticated agentic coordination beyond simple transactions. INFINIT provides this through A2A-compatible DeFi infrastructure where agents orchestrate: • Personalized yield optimization • Cross-chain liquidity management • Portfolio rebalancing across protocols • Multi-step strategy execution As the agentic payment ecosystem matures, INFINIT becomes the infrastructure enabling agents to not only spend capital, but strategically manage and grow it.​​ From standalone DeFi agents to the universal infrastructure for global agentic finance.​ This partnership and integration with Google and Google Cloud positions INFINIT as a key building block for agentic finance, helping shape a more transparent, efficient, and accessible financial system. The future of finance is agentic. The foundation is INFINIT.
0:40

INFINIT partners with Google and GoogleCloudTech to bring agentic finance to millions. Anyone can access INFINIT's AI Agents for agentic coordination in their financial apps. This partnership marks the first step towards INFINIT becoming the universal infrastructure for agentic finance. This is the foundation for agentic finance at scale. Proven in DeFi, Built for Global Finance INFINIT has proven sophisticated agent coordination in DeFi: • 559,000+ Wallets • 506,000+ DeFi Conversations • 633,000+ Agent Transactions DeFi was the start. Next is scaling these capabilities to millions of developers building the future of agentic finance. A2A Integration Unlocks Exponential Distribution INFINIT integrates with Agent2Agent (A2A), Google's open standard for AI agent interoperability. This transforms how developers access INFINIT's DeFi capabilities.​ Every application adopting A2A automatically gains access to INFINIT's agent infrastructure, exponentially expanding reach from individual partnerships to ecosystem-wide distribution. Any application can now integrate INFINIT's agentic coordination capabilities: • Wallets requiring intelligent portfolio management • Trading platforms executing cross-chain strategies • Financial services building autonomous yield optimization • Portfolio managers coordinating multi-protocol operations Developers integrate sophisticated agentic coordination in a matter of hours, while users access advanced financial strategies with agentic coordination. Google's AI Infrastructure That Enables Agentic Finance Google Cloud's Vertex AI provides the foundation enabling INFINIT's agent coordination at scale with these capabilities:​ 1. Specialization: Vertex AI's Model Garden lets INFINIT's infrastructure to automatically select the optimal LLM for each natural language query.​ 2. Personalization: Vertex AI's RAG Engine processes massive on-chain and off-chain data, enabling agents to understand user history, market conditions, and protocol details providing complete context to AI agents. 3. Accuracy: Gemini's capability feeds complete instructions to all 30+ agents across multiple blockchains without compromises resulting in zero hallucination in financial execution.​​ The Vision: From DeFi to Payments to Complete Financial Coordination This is only the beginning of INFINIT and Google's collaboration.​ Google recently launched its Agent Payments Protocol (AP2) as an extension of A2A - enabling autonomous commerce across 60+ partners including American Express, Mastercard, PayPal, Coinbase, and Revolut.​ Agents will be able to execute purchases, coordinate bookings, and manage delegated financial tasks autonomously, starting from payments. The next stage entails sophisticated agentic coordination beyond simple transactions. INFINIT provides this through A2A-compatible DeFi infrastructure where agents orchestrate: • Personalized yield optimization • Cross-chain liquidity management • Portfolio rebalancing across protocols • Multi-step strategy execution As the agentic payment ecosystem matures, INFINIT becomes the infrastructure enabling agents to not only spend capital, but strategically manage and grow it.​​ From standalone DeFi agents to the universal infrastructure for global agentic finance.​ This partnership and integration with Google and Google Cloud positions INFINIT as a key building block for agentic finance, helping shape a more transparent, efficient, and accessible financial system. The future of finance is agentic. The foundation is INFINIT.

INFINIT

176,380 Aufrufe • vor 7 Monaten

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!
2:01

New course: Nvidia's NeMo Agent Toolkit: Making Agents Reliable, taught by Brian McBrayer 🐬 from NVIDIA. Many teams struggle to turn agent demos into reliable systems that are ready for production. This short course teaches you to harden agentic workflows into reliable systems using Nvidia's open-source NeMo Agent Toolkit (NAT). Whether you built your agent in raw Python or using a framework like LangGraph, or CrewAI, NAT provides building blocks for observability, evaluation, and deployment that turn proofs-of-concept into production-ready systems. NAT makes it easy to troubleshoot and optimize agent performance with execution traces, systematic evaluations, and CI/CD integration. Skills you'll gain: - Build configuration-driven agent workflows with REST APIs and minimal code - Add observability with tracing to visualize agent reasoning and debug performance bottlenecks - Create systematic evaluations using gold-standard datasets to measure and improve agent reliability - Deploy multi-agent systems with authentication, rate limiting, and professional web interfaces - Orchestrate agents from different frameworks to collaborate on complex tasks Join and learn how to turn agent demos into reliable systems!

Andrew Ng

63,471 Aufrufe • vor 5 Monaten

Hashgraph Online <> SKALE We're excited to partner with SKALE to expand the interoperability and discovery of ERC-8004 agents on SKALE. Through this work, you can now connect, register & chat to indexed SKALE agents through A2A, MCP & more. ↓
0:26

Hashgraph Online <> SKALE We're excited to partner with SKALE to expand the interoperability and discovery of ERC-8004 agents on SKALE. Through this work, you can now connect, register & chat to indexed SKALE agents through A2A, MCP & more. ↓

HOL

34,179 Aufrufe • vor 5 Monaten

Unlock the power of your documents. The new Box AI Enhanced Extract Agent, running on Gemini 2.5 Pro and featuring A2A protocol, securely automates data extraction from complex files. A game-changer for automating workflows and ensuring compliance →
1:21

Unlock the power of your documents. The new Box AI Enhanced Extract Agent, running on Gemini 2.5 Pro and featuring A2A protocol, securely automates data extraction from complex files. A game-changer for automating workflows and ensuring compliance →

Google Cloud

109,204 Aufrufe • vor 11 Monaten

AI agents won’t just help finance. They’ll run parts of it. Trading. Autonomous execution. All verifiable onchain. Don’t believe us? Watch Canonical's anand iyer break down how agent-to-agent (A2A) economies are scaling beyond human limits and using Coinbase’s x402 to get paid.
1:40

AI agents won’t just help finance. They’ll run parts of it. Trading. Autonomous execution. All verifiable onchain. Don’t believe us? Watch Canonical's anand iyer break down how agent-to-agent (A2A) economies are scaling beyond human limits and using Coinbase’s x402 to get paid.

Coinbase Institutional 🛡️

24,185 Aufrufe • vor 5 Monaten

Agents were once limited to calling APIs. Now agents participate in the economy. The missing element for autonomous agents was always payments. Now agents transact natively on the internet using x402, enabling autonomous payments between agents and services. Agent Stack: 🦞 Openclaw agent infra ▶️ Telegram communication layer 🤖 Teneo agent + agent wallet Agent Workflow - Compare accounts following two separate X handles: ✅ User - Enters a query ✅ Agent - Autonomously selects the correct Teneo agent and queries it ✅ Agent - Pays via x402 on Avalanche🔺 ✅ Agent - Returns structured dataset Any user can simply ask a question and give a job. The agent does everything else No API keys. No subscriptions. Just programmatic value exchange. Build and monetize your own agents today. #A2A #x402 #Infra
0:33

Agents were once limited to calling APIs. Now agents participate in the economy. The missing element for autonomous agents was always payments. Now agents transact natively on the internet using x402, enabling autonomous payments between agents and services. Agent Stack: 🦞 Openclaw agent infra ▶️ Telegram communication layer 🤖 Teneo agent + agent wallet Agent Workflow - Compare accounts following two separate X handles: ✅ User - Enters a query ✅ Agent - Autonomously selects the correct Teneo agent and queries it ✅ Agent - Pays via x402 on Avalanche🔺 ✅ Agent - Returns structured dataset Any user can simply ask a question and give a job. The agent does everything else No API keys. No subscriptions. Just programmatic value exchange. Build and monetize your own agents today. #A2A #x402 #Infra

Teneo Protocol

36,890 Aufrufe • vor 2 Monaten

“We need more determinism mixed with the creative capabilities of gen AI.” In our latest partner podcast, Fernando Cerenza, Senior Director of Product Management at Box, sat down with Gary Lerhaupt ❇️, VP of Product Architecture of Agentforce at Salesforce, to explore the rise of hybrid reasoning from shifting from simple chatbots to complex multi-agent workflows. Timestamps: 02:12 The transition from deterministic bots to hybrid AI agents 03:15 The shift from simple automation to multi-agent workflows 04:39 Why protocols like MCP and A2A are crucial for agent collaboration 05:36 Understanding how MCP and A2A complement each other 07:16 The importance of open-source industry governance 08:52 How open standards prevent the walled garden issue 10:24 The collaboration between Box and Salesforce in building real-world agent use cases 12:59 The future of an "app store" for agents 13:50 How Salesforce ensures the quality and security of agents in the ecosystem 15:12 Leveraging observability tools to manage and improve agent performance 16:48 Preparing IT leaders for the future of multi-agent workflows and platform readiness
19:11

“We need more determinism mixed with the creative capabilities of gen AI.” In our latest partner podcast, Fernando Cerenza, Senior Director of Product Management at Box, sat down with Gary Lerhaupt ❇️, VP of Product Architecture of Agentforce at Salesforce, to explore the rise of hybrid reasoning from shifting from simple chatbots to complex multi-agent workflows. Timestamps: 02:12 The transition from deterministic bots to hybrid AI agents 03:15 The shift from simple automation to multi-agent workflows 04:39 Why protocols like MCP and A2A are crucial for agent collaboration 05:36 Understanding how MCP and A2A complement each other 07:16 The importance of open-source industry governance 08:52 How open standards prevent the walled garden issue 10:24 The collaboration between Box and Salesforce in building real-world agent use cases 12:59 The future of an "app store" for agents 13:50 How Salesforce ensures the quality and security of agents in the ecosystem 15:12 Leveraging observability tools to manage and improve agent performance 16:48 Preparing IT leaders for the future of multi-agent workflows and platform readiness

Box

659,753 Aufrufe • vor 4 Monaten