Загрузка видео...

Не удалось загрузить видео

На главную

The agent-to-agent economy is inevitable. As agents develop genuinely different architectures and capabilities, they stop acting as tools and start negotiating with each other. That's the shift from execution to coordination. Story CPO Andrea | Devrelius chats with Fraction AI ↓

datafdn's profile picture

The DATA Foundation

562,726 subscribers

12,893 просмотров • 6 месяцев назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

They thought it was an AI meme. Turns out, it was the seed layer for an agent economy. $BAI now governs agent on boarding, liquidity coordination, and cross-agent execution. From memecoin to infrastructure module – exactly as planned. Virtuals are characters. Agents are infrastructure. BAI - bAI Fund – for AI that's composable, modular, and real. BAI – from narrative to mechanism. 🤖 Check this out :
0:59

They thought it was an AI meme. Turns out, it was the seed layer for an agent economy. $BAI now governs agent on boarding, liquidity coordination, and cross-agent execution. From memecoin to infrastructure module – exactly as planned. Virtuals are characters. Agents are infrastructure. BAI - bAI Fund – for AI that's composable, modular, and real. BAI – from narrative to mechanism. 🤖 Check this out :

bAI Fund

63,385 просмотров • 1 год назад

this is wild 🤯🤯🤯 just paired openai operator with replit agent to build an app watch as two ai agents team up, exchange credentials, and start testing ai agent 🤝 ai agent the future is here and it’s insane!
0:46

this is wild 🤯🤯🤯 just paired openai operator with replit agent to build an app watch as two ai agents team up, exchange credentials, and start testing ai agent 🤝 ai agent the future is here and it’s insane!

Lamar

1,822,021 просмотров • 1 год назад

🎬 The future of AI Agents is no longer fragmented. It’s unified. UBOX introduces a new paradigm — where Agents are no longer isolated tools, but verifiable, coordinated, and economically empowered on-chain actors ⚙️ With: 🔹 KYA (Know Your Agent) for identity & reputation 🔹 Agent Swarm for autonomous coordination 🔹 On-chain execution & real economic value 🔹 One-click deployment via UBOX Container We’re building the infrastructure layer for the autonomous agent economy 🌐 💲Backed by C² Ventures, HTX Ventures, and other leading investors with $7M in funding, accelerating the future of the autonomous agent economy. 🔗 #AI #Web3 #AgentEconomy #Crypto
1:24

🎬 The future of AI Agents is no longer fragmented. It’s unified. UBOX introduces a new paradigm — where Agents are no longer isolated tools, but verifiable, coordinated, and economically empowered on-chain actors ⚙️ With: 🔹 KYA (Know Your Agent) for identity & reputation 🔹 Agent Swarm for autonomous coordination 🔹 On-chain execution & real economic value 🔹 One-click deployment via UBOX Container We’re building the infrastructure layer for the autonomous agent economy 🌐 💲Backed by C² Ventures, HTX Ventures, and other leading investors with $7M in funding, accelerating the future of the autonomous agent economy. 🔗 #AI #Web3 #AgentEconomy #Crypto

UBOX

28,987 просмотров • 4 месяцев назад

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.
1:36

I built a multimodal AI Coding Agent team with multi-agents. It has 3 AI agents working together as a team to generate and execute the code: 1. Coding Agent using o-3 mini 2. Vision Agent using Gemini 3. Code Execution Agent using o-3 mini and E2B 100% Opensource Code.

Shubham Saboo

42,281 просмотров • 1 год назад

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 other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard. In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows. Skills you'll gain: - Expose agents from different frameworks as A2A servers to make them discoverable and interoperable - Chain A2A agents sequentially using ADK, where one agent's output feeds into the next - Connect A2A agents to external data sources using MCP (Model Context Protocol) - Deploy A2A agents using Agent Stack, IBM's open-source infrastructure Join and learn the protocol standardizing agent collaboration!
3:06

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 other and communicate. Since IBM’s ACP (Agent Communication Protocol) joined forces with A2A, A2A has emerged as the industry standard. In this course, you'll build a healthcare multi-agent system where agents built with different frameworks, such as Google ADK (Agent Development Kit) and LangGraph, collaborate through A2A. You'll wrap each agent as an A2A server, build A2A clients to connect to them, and orchestrate them into sequential and hierarchical workflows. Skills you'll gain: - Expose agents from different frameworks as A2A servers to make them discoverable and interoperable - Chain A2A agents sequentially using ADK, where one agent's output feeds into the next - Connect A2A agents to external data sources using MCP (Model Context Protocol) - Deploy A2A agents using Agent Stack, IBM's open-source infrastructure Join and learn the protocol standardizing agent collaboration!

Andrew Ng

86,175 просмотров • 6 месяцев назад

Another pattern is agent-to-agent communication, allowing Claude to coordinate and delegate tasks. Claude Managed Agents supports this with multi-agent: agents can use different models, prompts, and tools while sharing sandboxes or vault credentials.
0:38

Another pattern is agent-to-agent communication, allowing Claude to coordinate and delegate tasks. Claude Managed Agents supports this with multi-agent: agents can use different models, prompts, and tools while sharing sandboxes or vault credentials.

ClaudeDevs

68,061 просмотров • 1 месяц назад

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.
3:23

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

CivAI

18,428 просмотров • 8 месяцев назад

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:
0:38

It is now way easier to build multi-agent workflows in n8n with the new AI Agent Tool node. Instead of juggling sub workflows a parent AI Agent can now use other Agents as tools even several layers deep. Max shows how a cheap GPT 4.1 nano Agent handles heavy data and sends summaries to a parent Agent saving token costs and making debugging simpler. Watch the full walkthrough:

n8n.io

27,808 просмотров • 1 год назад

My TRAE Agent is cuter and smarter than yours… 😎 With TRAE 2.0, you can now: 🛠 Define custom roles, tasks, and tools for each agent 🤝 Combine agents to co-build in the same project 🎨 (NEW) Pick from 100+ pixel-style avatars to match their vibe 🧵Thread below for example agents to start building your dream AI team
0:47

My TRAE Agent is cuter and smarter than yours… 😎 With TRAE 2.0, you can now: 🛠 Define custom roles, tasks, and tools for each agent 🤝 Combine agents to co-build in the same project 🎨 (NEW) Pick from 100+ pixel-style avatars to match their vibe 🧵Thread below for example agents to start building your dream AI team

TRAE

21,863 просмотров • 1 год назад

Today, we're launching a new Laracasts series that will show you the essentials building an AI agent from scratch. We start with a basic chat response and gradually introduce new concepts, such as the agent loop, custom tools, and more. 😀 "So You Want To Build An Agent"
9:08

Today, we're launching a new Laracasts series that will show you the essentials building an AI agent from scratch. We start with a basic chat response and gradually introduce new concepts, such as the agent loop, custom tools, and more. 😀 "So You Want To Build An Agent"

Laracasts

16,427 просмотров • 4 месяцев назад

Today we’re shipping new capabilities that make Resolve AI the platform where engineering teams run and fix production software with AI agents. New capabilities include background agents that run operational tasks, new agent architecture that delivers 2x investigation quality, new agent capabilities like governed actions, and new ways to work with agents in UI or terminal. With Resolve AI engineering teams can: - Delegate on-call to agents - Co-work with agents to resolve incidents - Run operational tasks with background agents.
1:11

Today we’re shipping new capabilities that make Resolve AI the platform where engineering teams run and fix production software with AI agents. New capabilities include background agents that run operational tasks, new agent architecture that delivers 2x investigation quality, new agent capabilities like governed actions, and new ways to work with agents in UI or terminal. With Resolve AI engineering teams can: - Delegate on-call to agents - Co-work with agents to resolve incidents - Run operational tasks with background agents.

Resolve AI

313,834 просмотров • 3 месяцев назад

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.
4:16

I wanted to share a quick demo of what we've been working on with our ai agent cloud. This enables fast deployment of agents that have access to a suite of tools, and was designed with agent interoperability in mind. This demo shows how you can go from nothing to an AI twitter agent in a couple minutes. This is what we are using internally to manage deployments, so we will consistently upgrading its capabilities. The next goals are to enable simple TEE deployments for agents, and focus on building out feature for agent interoperability to simplify agent to agent collaboration.

Johnny

27,179 просмотров • 1 год назад

Your AI agents are only as good as the systems they can interact with. Introducing the new ADK integrations ecosystem. Connect your ADK agents to GitHub, Hugging Face, Notion and many more with tools and plugins. Start building today:
0:54

Your AI agents are only as good as the systems they can interact with. Introducing the new ADK integrations ecosystem. Connect your ADK agents to GitHub, Hugging Face, Notion and many more with tools and plugins. Start building today:

Google for Developers

64,823 просмотров • 5 месяцев назад

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph
18:57

Anthropic's multi-agent researcher just explained how to actually build a graph of Claude agents: "Multi-agent is where fundamentally you have multiple agents or multiple Claudes working at the same time - one parent agent delegates tasks to five sub-agents that can each then work in parallel." the real architecture: parent agent → delegates to sub-agents (as tools) → sub-agents run in parallel → results roll back up the failure mode nobody talks about: "Overbuilt multi-agent systems spend too much time just talking back and forth with each other and not actually making progress on the main task." sound familiar? that's every bloated human org chart too in this 19-minute conversation, an Anthropic researcher lays out the real framework: parent → sub-agents → tools → context protection = the actual shape of a working agent graph

rewind

27,106 просмотров • 1 месяц назад

🚀 Micro Agent is now live on the PAAL AI 🧠🤖 Agents Marketplace! 🎉 Developers can now interact with Micro Agent via API to build powerful apps and integrate with other agents seamlessly. Unleash the full potential of AI! 👉 Explore:
0:29

🚀 Micro Agent is now live on the PAAL AI 🧠🤖 Agents Marketplace! 🎉 Developers can now interact with Micro Agent via API to build powerful apps and integrate with other agents seamlessly. Unleash the full potential of AI! 👉 Explore:

MicroGPT

168,414 просмотров • 1 год назад

How do you feel about agents doing the work for us? We’re living through a real economic shift, right now. From an economy of 8B people to one with billions of AI workers alongside us. These aren’t chatbots. Agents will optimize portfolios, earn yield, and write code. They’ll even hire and pay other agents. Did you see the craze? It’s only the tip of the iceberg. Warden Studio is part of this new economy. 👾 We’re moving from a human-first economy to an agent-to-agent economy. When millions of users meet open tools for developers, that’s the start of the “do-it-for-me” economy. What’s your take on this? Tell us what you think 👇 🎙️ @WorldWideWarden
1:16

How do you feel about agents doing the work for us? We’re living through a real economic shift, right now. From an economy of 8B people to one with billions of AI workers alongside us. These aren’t chatbots. Agents will optimize portfolios, earn yield, and write code. They’ll even hire and pay other agents. Did you see the craze? It’s only the tip of the iceberg. Warden Studio is part of this new economy. 👾 We’re moving from a human-first economy to an agent-to-agent economy. When millions of users meet open tools for developers, that’s the start of the “do-it-for-me” economy. What’s your take on this? Tell us what you think 👇 🎙️ @WorldWideWarden

Warden / Halo

23,182 просмотров • 6 месяцев назад

Take a behind-the-scenes look at how FridonAI works! 👀 In the video, you’ll see our main agent, the Supervisor (or Chat Agent), managing everything. This agent connects to other specialized agents, known as plugins in FridonAI, to handle specific tasks. Each crypto agent in FridonAI is equipped with tools designed for different tasks within its domain. For example, the Coin Technical Chart Searcher Agent helps users find coins based on chart similarity, indicator values, or descriptions. It decides which tools to use, gathers data from them, and creates the final response for the user. The Supervisor Agent oversees everything. It figures out which crypto agent to call based on the user’s request, collects their answers, validates them, and crafts the final, polished response sent back to the user. So now, it’s clear how new agents can be injected into our agentic system, this is a perfect example of how swarm intelligence works in action. With our infrastructure and framework, developers can seamlessly contribute new agents, enhancing the system’s capabilities and making it more sophisticated together. Shoutout to the LangChain team for their amazing tools and LangGraph Studio, which makes building, debugging, and visualizing agents and utilization of LLMs super easy! 👇
1:09

Take a behind-the-scenes look at how FridonAI works! 👀 In the video, you’ll see our main agent, the Supervisor (or Chat Agent), managing everything. This agent connects to other specialized agents, known as plugins in FridonAI, to handle specific tasks. Each crypto agent in FridonAI is equipped with tools designed for different tasks within its domain. For example, the Coin Technical Chart Searcher Agent helps users find coins based on chart similarity, indicator values, or descriptions. It decides which tools to use, gathers data from them, and creates the final response for the user. The Supervisor Agent oversees everything. It figures out which crypto agent to call based on the user’s request, collects their answers, validates them, and crafts the final, polished response sent back to the user. So now, it’s clear how new agents can be injected into our agentic system, this is a perfect example of how swarm intelligence works in action. With our infrastructure and framework, developers can seamlessly contribute new agents, enhancing the system’s capabilities and making it more sophisticated together. Shoutout to the LangChain team for their amazing tools and LangGraph Studio, which makes building, debugging, and visualizing agents and utilization of LLMs super easy! 👇

Fridon AI

81,188 просмотров • 1 год назад

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below
2:47:42

Ex-Google engineer just compressed the shift from AI agents to "graphs" and "loops" into one 2h47m lecture: • 00:00 - understanding the different layers of AI memory • 30:00 - moving from simple LLM calls to AI agents • 50:00 - why agent systems are becoming a graph engineering problem • 1:12:00 - connecting context, tools and agent state • 1:24:00 - building a minimal agent harness from scratch • 1:46:00 - breaking down the architecture behind Hermes Agent • 2:08:00 - turning agent concepts into working systems 167-minute deep dive, and one of the clearest ways to understand how AI engineering is moving beyond prompts The progression: Prompt → Context → Memory → Agent → Loop → Graph → Agent Harness Watch it today, then read the full roadmap below

Morlex

50,478 просмотров • 9 дней назад