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LLMs can hallucinate URLs. 404s happen. 🛠️ See how Google's Agent Development Kit (ADK) uses a built-in web UI to debug agents in real time and orchestrate multi-agent systems to verify data. Watch the full tutorial →

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22,630 görüntüleme • 1 ay önce •via X (Twitter)

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🚨 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
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🚨 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

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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 →
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Just dropped: An in-depth guide to connecting Fetch.ai uAgents and Google AI ADK (Agent Development Kit) 🧠⚡ In our latest dev showcase, we connect uAgents with Google’s Agent Development Kit (ADK) for Google for Developers to build intelligent agent systems. Learn more: Watch an in-depth walkthrough👇
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Just dropped: An in-depth guide to connecting Fetch.ai uAgents and Google AI ADK (Agent Development Kit) 🧠⚡ In our latest dev showcase, we connect uAgents with Google’s Agent Development Kit (ADK) for Google for Developers to build intelligent agent systems. Learn more: Watch an in-depth walkthrough👇

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Move from idea to production-ready agents in hours with the updated ADK and our AI-native coding skills. Use Agent Studio to convert prompts into functional agents, or leverage Agent Garden's pre-built blueprints to jumpstart your multi-agent systems →
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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!
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🚨 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

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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 > LangGraph & Google ADK agents working together simultaneously inside an app Demo GitHub, docs, and more 👇

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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!
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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!

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👾Watch a multi-agent system built with Gemini 3.6 Flash iterate on playable game design in real time. It uses Gemini 3.5 Flash-Lite to design, construct, and verify puzzle challenges based on live player actions balancing speed and reasoning in a fast-paced environment.

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OpenAI's Swarm Web Extractor This can autonomously search the web, map entire websites, and extract data. This is built on top of OpenAI’s new multi-agent framework Swarm, Serp AI and Firecrawl API. - Swarm is a lightweight and experimental framework introduced by OpenAI to develop multi-agent systems. - Serp API A is a real-time API that allows users to access Google search results. - Firecrawl API turns entire websites into clean, LLM-ready markdown or structured data. Scrape, crawl and extract the web with a single API. Video credits: Eric Ciarla #webextraction #llms #nlproc #swarm #multiagents

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Cursor can now fix your hardest bugs. Debug Mode instruments your code, spins up a server to capture logs, and streams runtime data to the agent. Also in 2.2: Plan Mode improvements, multi-agent judging, and more.
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Cursor can now fix your hardest bugs. Debug Mode instruments your code, spins up a server to capture logs, and streams runtime data to the agent. Also in 2.2: Plan Mode improvements, multi-agent judging, and more.

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This morning we shared Google launched AP2 (Agent Payment Protocol) with EigenCloud as a launch partner. Now see it in action: we’ve extended Google's Agent Development Kit demo to show natural-language payments + storage on EigenDA, agents can store/retrieve data, pay with crypto rails (X402), and get back proofs instantly. Agents can now store + pay on EigenDA… just by asking in plain English 🤯 Try it yourself 👇

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Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

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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:

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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!

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Pydantic AI Agents: NEW Multi-Agent Framework 📚 Step-by-step Hands-on Tutorial 🔍 How to Install PydanticAI? 🛠️ What Models are Supported? Used Groq Inc 🎯 How to Create First Agent? ⚡ How to Add Custom Tools? 🔌 Built-in validation 🎮 How it differs from CrewAI or AutoGen? Pydantic Samuel Colvin jason liu

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