A single AI agent can't be an expert in everything. Our new labs show how to orchestrate multi-agent systems where specialized agents collaborate to solve complex problems.
Learn to use the MCP as a router for tasks and enable direct A2A communication ↓ https://t.co/IFqMc01Vkj
So you've built an AI Agent. Now, learn how to deploy it with our new set of hands-on labs.
You'll get hands-on experience deploying your agent with:
⚙️ Agent Engine
🚀 Cloud Run
☸️ GKE
Learn more and grab the links to all three labs, here ↓ https://t.co/x65AAnxIC6
Build a production-ready AI security foundation with three news labs.
Get hands-on experience with:
🛡️ Securing your training data
🔒 Hardening the AI infrastructure
🕵️ Controlling inputs and outputs to prevent abuse
Check them out ↓ https://t.co/BmorMPTBIG
How do you evaluate if your gen AI application is actually good?
Our new hands-on labs are designed to help you master AI evaluation—from single prompts and RAG systems to complex AI agents. Check out this blog for more info and links to all four labs ↓ https://t.co/9JXkdFXClT
This was so easy, it's almost embarrassing.
With one prompt, can I get @antigravity to design and build a data schema for a multi-tenant SaaS billing system?
Yes. And it uses its browser, agents, and terminal to do it.
Try it yourself. It's awesome.
https://t.co/UUp0lO3Gbf
Open standards for agentic AI, like MCP and A2A, are great to have and even more so if you know how to use them. Check out this blog post with some labs to get you started.
For those who want to dive straight into the code, here are the direct links to the labs. 🤓
- Getting Started with MCP, ADK and A2A 👉https://t.co/L0MHBcz5Nv
- MCP Toolbox for BigQuery Databases 👉https://t.co/uL2ijQTTJ6
- Build a Travel Agent using MCP Toolbox 👉https://t.co/RzHZJpxIE9
#HandsOnLab #AIAgents
A single AI agent, no matter how powerful, can't be an expert in everything.
To help you build more sophisticated multi-agent systems, I'm sharing a blog post that introduces hands-on labs from my colleagues @JackWoth98 & @iRomin. 🧵👇
https://t.co/CdNW78UP9o
@GoogleCloudTech #ProductionReadyAI
In these labs, you'll get hands-on experience facilitating communication between #AIAgents by:
🤝 Implementing the Agent-to-Agent (#A2A) Protocol
📡 Using the Model Context Protocol (#MCP) as a router for tasks
As a data scientist focused on SFT, I’ve always wondered where RL truly fits in and where do TPUs really shine.
Joining Google finally gave me the answers!
Level up your AI strategy with our latest #AgentFactory recap:
https://t.co/X0jSUX5jsR
For those who want to dive straight into the code, here are the direct links to the labs. 🤓
- Evaluate Single LLM Outputs: https://t.co/hEzUHq5jKp
- Evaluate RAG: https://t.co/EGPLstaobg
- Evaluating Agents: https://t.co/PWUBvRU8ZU
- Evaluate BigQuery Agents: https://t.co/CNfnywa9UO
#HandsOnLab
How do you know your #GenAI app is actually good? A "vibe check" isn't enough for production.
To help, my colleagues @SmithaKolan, @anniewangtech, & Rachael Deacon-Smith created 4 new hands-on labs to help you master #AIEvaluation.
This blog post introduces them all. ���
https://t.co/PQ4dzgIBdB
@GoogleCloudTech
In these labs, you'll get hands-on with:
📏 Evaluating Single Prompts with Vertex AI
🔎 Evaluating RAG Systems with Vertex AI
📈 Evaluating Agents with ADK
🤖 Evaluating BigQuery Agents with ADK and GenAI Eval Service
#HandsOnLab#ProductionReadyAI
You have a genuinely dumb idea for an app. Will your favorite AI tool build it anyway?
I wondered if @antigravity would. It did, but also helped me go back, refine my thinking, and land on something useful.
https://t.co/Txntq5N8qu
Happy New Y{AI}r! 🥂
I believe this is the year Multi-Agent Systems become more popular
I recently tried to build a Deep Research tool and realized that a single agent is… slow.
So I built a squad. Here’s how I cut latency by 60% with #GoogleADK & #CloudRun. 🧵👇
Looking for a New Year's Resolution? 📝 You couuuuld make 2026 the year you learn production-ready #AI.
To help you with this, I'm resharing the Production-Ready AI learning path, a self-paced curriculum to take your #GenAI skills to the next level.
Start your resolution here 🤗🤓🧑💻👇
https://t.co/PgsKs0hELC
@GoogleCloudTech #LearnToCode
Ready to build AI that can reason, plan, and act? Easily build your own AI agents, even without deep expertise, thanks to Agent Development Kit (ADK)!
This blog introduces three hands-on labs that cover the core patterns of agent development → https://t.co/2sifPDkHa7
A massive amount of work went into this from my colleagues and me to create a practical learning path for production AI.
From building your first #AIAgent to securing it and deploying it to the cloud. Hope you find it useful!
#ProductionReadyAI#AIApps
Introducing the Production-Ready AI with Google Cloud Learning Path, a free series designed to take your AI projects from prototype to production.
The path's curriculum combines Gemini models with #VertexAI, GKE, and more. Check out the modules, here ↓ https://t.co/YolKKvXVBg