Video yükleniyor...

Video Yüklenemedi

Ana Sayfaya Dön

It’s getting insanely easy to build complex multi-agent systems in n8n. You can now build supervisor agents that delegates tasks to specialized sub agents. Just add the sub agents via AI Agent Tool node. All in one place and a lot easier to debug! Zero code!

omarsar0's profile picture

elvis

298,230 subscribers

45,690 görüntüleme • 1 yıl önce •via X (Twitter)

Sağlık & İyilikBilim & TeknolojiEğitim
LIVE

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

🚨 Agent Swarms - Multi-Agents Delegate Complex Prompts To Sub-Agents Use Gemini 3.5 Flash, Opus 4.7 and GPT 5.5 xHIgh to create complex multi-agent system A master agent can orchestrate several worker agents to just do things Build full-stack apps, mobile apps and automate complex scheduled tasks Each agent can do different tasks including Q/A, monitoring and software development
1:05

🚨 Agent Swarms - Multi-Agents Delegate Complex Prompts To Sub-Agents Use Gemini 3.5 Flash, Opus 4.7 and GPT 5.5 xHIgh to create complex multi-agent system A master agent can orchestrate several worker agents to just do things Build full-stack apps, mobile apps and automate complex scheduled tasks Each agent can do different tasks including Q/A, monitoring and software development

Abacus.AI

82,823,618 görüntüleme • 3 ay önce

Why have one AI agent handle everything when you can build a team? 🤝 Multi-agent architecture allows a main agent to spin up specialized sub-agents and handle complex tasks in parallel - making workflows faster and more efficient inside Google AI Studio. Check out the full I/O session: 🧵👇
1:04

Why have one AI agent handle everything when you can build a team? 🤝 Multi-agent architecture allows a main agent to spin up specialized sub-agents and handle complex tasks in parallel - making workflows faster and more efficient inside Google AI Studio. Check out the full I/O session: 🧵👇

Google for Developers

12,450 görüntüleme • 2 ay önce

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 görüntüleme • 1 yıl önce

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

27,195 görüntüleme • 1 ay önce

Claude Code sub-agents is probably the easiest way to build complex, custom agentic systems. Not just for code, but for anything you can imagine. Watch me build a multi-agent deep research system with Claude Code subagents. Chain subagents with /commands for reliability.
5:07

Claude Code sub-agents is probably the easiest way to build complex, custom agentic systems. Not just for code, but for anything you can imagine. Watch me build a multi-agent deep research system with Claude Code subagents. Chain subagents with /commands for reliability.

elvis

190,095 görüntüleme • 1 yıl önce

It’s much easier to build an AI agent, if all you have to do is prompt it! The backend, code execution and deployment are all handled automatically. Even non-techies can build AI agents
0:30

Sensitive content

It’s much easier to build an AI agent, if all you have to do is prompt it! The backend, code execution and deployment are all handled automatically. Even non-techies can build AI agents

Bindu Reddy

51,180 görüntüleme • 1 yıl önce

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!
2:39

New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report, you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!

Andrew Ng

350,175 görüntüleme • 2 yıl önce

🚀 LAUNCHING: Genspark Multi-Agent Orchestration! One lead agent coordinates multiple sub-agents to tackle complex tasks. Watch the demo: Create 10 agents simultaneously - 10 professional slide decks in the time it takes to make one. Experience it: Blog:
0:45

🚀 LAUNCHING: Genspark Multi-Agent Orchestration! One lead agent coordinates multiple sub-agents to tackle complex tasks. Watch the demo: Create 10 agents simultaneously - 10 professional slide decks in the time it takes to make one. Experience it: Blog:

Genspark

23,144 görüntüleme • 1 yıl önce

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!
9:09

The best way to learn AI is to build with agents. To help with that, we've launched hands-on labs and a new series on Agentic Engineering. First topic: Agent Skills. Next in the pipeline: planning, context engineering, multi-agent systems, long-running agents,.. Go build!

elvis

31,802 görüntüleme • 3 ay önce

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:
2:23

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

204,109 görüntüleme • 8 ay önce

🚨 Breaking: Google just open-sourced the Agent Development Kit (ADK) a framework for building AI agents and multi-agent systems. - Build agents in under 100 lines. - Supports MCP More information and how to get started 👇 1/5
0:22

🚨 Breaking: Google just open-sourced the Agent Development Kit (ADK) a framework for building AI agents and multi-agent systems. - Build agents in under 100 lines. - Supports MCP More information and how to get started 👇 1/5

AshutoshShrivastava

130,272 görüntüleme • 1 yıl önce

Introducing Electric Agents! Agents are not compute. Agents are data. Multi-agent is a sync problem. Electric Agents is the first agent platform built on sync. Use it to build scalable, collaborative, long-lived multi-agent systems: 🧵👇
0:35

Introducing Electric Agents! Agents are not compute. Agents are data. Multi-agent is a sync problem. Electric Agents is the first agent platform built on sync. Use it to build scalable, collaborative, long-lived multi-agent systems: 🧵👇

Electric

81,539 görüntüleme • 4 ay önce

Deploy Your Agents Across Multiple Networks ⚙️ Build with Zero Code: Our no-code agent builder lets you create and deploy powerful agents in just minutes. Automate Smarter, Faster, and Easier. Join Sentio today and unlock the future of Web3 automation! #AI #SEN #AGENTS
0:20

Deploy Your Agents Across Multiple Networks ⚙️ Build with Zero Code: Our no-code agent builder lets you create and deploy powerful agents in just minutes. Automate Smarter, Faster, and Easier. Join Sentio today and unlock the future of Web3 automation! #AI #SEN #AGENTS

Sentio

45,479 görüntüleme • 1 yıl önce

Build smarter AI agents, faster. ⚡️ The open-source Agent Development Kit makes it easy to create modular, multi-agent systems using the models and tools you prefer. Join the ADK Hackathon →
0:51

Build smarter AI agents, faster. ⚡️ The open-source Agent Development Kit makes it easy to create modular, multi-agent systems using the models and tools you prefer. Join the ADK Hackathon →

Google for Developers

11,240 görüntüleme • 1 yıl önce

Anyone can build an AI agent in 60 minutes. Stanford just dropped a course on building AI agents from scratch. • 00:00 - Build your first AI agent • 48:17 - AI agents without coding • 54:39 - $100k+/month with agents While you scroll, someone's learning Anthropic's $750,000 skill. Watch it, then use the guide below to build your agent that prompts itself.
1:08:55

Anyone can build an AI agent in 60 minutes. Stanford just dropped a course on building AI agents from scratch. • 00:00 - Build your first AI agent • 48:17 - AI agents without coding • 54:39 - $100k+/month with agents While you scroll, someone's learning Anthropic's $750,000 skill. Watch it, then use the guide below to build your agent that prompts itself.

Raytar

50,165 görüntüleme • 20 gün önce

About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:
1:04

About a year ago, we launched the Agent-to-Agent protocol because AI agents shouldn’t work in silos. Today, developers are using A2A to coordinate specialized agents across enterprise data systems, commerce workflows, DevOps environments, and scientific research. FoldRun is one example, simplifying petabyte-scale protein modeling through agent-to-agent collaboration. Learn how to build with it in the blog:

Google for Developers

35,497 görüntüleme • 2 ay önce

THE FUTURE OF WORK: AI AGENT ORG CHARTS ARE HERE Companies are creating "AI heads of departments" — each managing 5–7 sub-agents to handle tasks just like a real team. Execs delegate to a chief agent, who routes tasks to department heads, who then deploy specialized sub-agents. Employees access their department's agent through a chatbot or voicebot — making AI as easy to use as Slack. Source: benjamlns on IG
1:14

THE FUTURE OF WORK: AI AGENT ORG CHARTS ARE HERE Companies are creating "AI heads of departments" — each managing 5–7 sub-agents to handle tasks just like a real team. Execs delegate to a chief agent, who routes tasks to department heads, who then deploy specialized sub-agents. Employees access their department's agent through a chatbot or voicebot — making AI as easy to use as Slack. Source: benjamlns on IG

Mario Nawfal

1,579,545 görüntüleme • 1 yıl önce

China just dropped a free AI super agent that can build software for you. It’s called DeerFlow 2.0. And it’s exploding on GitHub right now. This thing doesn’t just chat. You give it a goal. - It researches the problem. - Writes the code. - Creates files. - Launches sub-agents. Then delivers a finished result. Basically… an AI dev team in one tool.
0:29

China just dropped a free AI super agent that can build software for you. It’s called DeerFlow 2.0. And it’s exploding on GitHub right now. This thing doesn’t just chat. You give it a goal. - It researches the problem. - Writes the code. - Creates files. - Launches sub-agents. Then delivers a finished result. Basically… an AI dev team in one tool.

Julian Goldie SEO

231,759 görüntüleme • 5 ay önce

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!
2:17

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

127,724 görüntüleme • 1 yıl önce