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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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🚨 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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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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👾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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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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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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