10 Google Cloud Codelabs to Build AI Agents in 2026
if you’re serious about learning how to build AI agents on Google Cloud, there’s no faster path than hands-on labs.
Google Cloud Next’s codelabs give you real infrastructure, real tools, and real deployments ,not toy examples. This year’s lineup is built around one theme: AI agents on GCP. From building your first agent with the Google Cloud Agent Builder to deploying multi-agent systems on Agent Engine, these 10 labs cover the full stack.
Here’s exactly where to start.
1. Build Rich AI Agent Experiences with ADK + A2UI
The Agent Development Kit (ADK) is Google’s open-source Python framework for building AI agents on Google Cloud. This codelab pairs it with A2UI — a UI layer that makes agent interactions feel natural and responsive.
Improve user interaction through intuitive, high-quality interfaces that allow users to interact with agentic systems seamlessly. If you’ve been wondering how to build a AI agent that feels polished rather than prototype-grade, this is the lab to start with.
👉 Start the ADK + A2UI codelab –https://goo.gle/41UfUBp
2. Build a Multi-Agent System on Google Cloud
Single agents are powerful. Multi-agent systems are transformative.
This codelab shows you how to create the architecture required to make multiple agents work together to achieve a shared goal — a researcher agent, a writer agent, an orchestrator that coordinates them both. It’s one of the clearest demonstrations of the 4 ways to build an AI agent at scale: from simple assistants to coordinated agent networks.
👉 Start the multi-agent system codelab-https://goo.gle/48R8aE4
3. Beyond the Simple SELECT: Natural Language to SQL with AlloyDB
One of the most practical Google Cloud AI solutions for enterprise teams: letting users query databases in plain English.
Democratize data access by building systems that allow users to query complex databases using natural language, supported by high-speed vector search. It’s a direct example of how Google Cloud AI services reduce the barrier between business users and their data.
👉 Start the AlloyDB NL2SQL codelab- https://goo.gle/4cG6wWX
4. Beat Fraud with an AI Shield — Spanner and BigQuery Graph
This lab walks through building a real-time fraud detection system using Spanner and BigQuery Graph databases. Analyze complex relationships in your data to prevent fraud at the point of transaction — before it completes.
It’s one of the strongest examples of digital transformation using AI/ML with Google Cloud in the financial services space. The same pattern applies to healthcare, logistics, and e-commerce.
👉 Start the fraud detection codelab-https://goo.gle/42o9FWK
5. Build Secure AI Agents on Google Cloud
Security isn’t optional when you’re deploying AI agents that access sensitive data or take real-world actions.
Protect the reasoning engine with Model Armor and IAM to manage agent access and ensure that sensitive data remains protected during execution. This is the lab that separates production-ready Google Cloud AI agents from demos.
👉 Start the secure agents codelab- https://goo.gle/42xTru5
6. Ground AI Agents with Google Maps Platform
Location context makes agents dramatically more useful for logistics, delivery, field operations, and any geography-dependent workflow.
Use geo-intelligent logistics to ground your agents in real-world location data to optimize field operations in real-time. It’s a concrete example of how Google Cloud AI tools extend beyond text into the physical world.
👉 Start the Google Maps grounding codelab-https://goo.gle/4e7OU90
7. Deploy and Scale Agents on Agent Engine
Building an agent locally is step one. Getting it to production — reliably, at scale — is the real challenge.
Agent Engine is Google Cloud’s managed runtime for deploying AI agents as containerized microservices that scale dynamically with your workload. This codelab takes you from a working agent to a deployed, auto-scaling service — the definitive guide to how Google Cloud AI agents move from prototype to production.
👉 Start the Agent Engine deployment codelab–https://goo.gle/4sUbVQw
8. The Ultimate Guide to Cloud Run: From Zero to Production
Not every agent needs Agent Engine. For custom containerized workloads, Cloud Run is the fastest path from local code to a live, scalable endpoint.
Use this lab as a blueprint for moving from a local prototype to a production-ready, auto-scaling platform on Cloud Run. If you want to know how to build AI agents for free (or near-free), Cloud Run’s generous free tier makes it the most cost-effective option for early-stage projects.
👉 Start the Cloud Run zero-to-production codelab-https://goo.gle/48n006f
9. Developer Keynote: Building Agents with Skills
This session goes deeper into the architecture of production AI agents — covering the Agent Development Kit (ADK), prompting, Agent Skill usage, and MCP.
Learn the ins and outs of AI agent development, including how prompting, tool use, and multi-step reasoning work together in a real system. Essential context before you deploy anything serious on the Google Cloud AI platform
👉 Watch the Developer Keynote: Building Agents with Skills- https://goo.gle/4tsqZFV
10. General Keynote: Forecasting with AI Agents
The final lab demonstrates how AI agents can transform unstructured chaos into actionable business intelligence in seconds.
This is where Google Cloud AI solutions meet real business value: not just answering questions, but predicting outcomes. A strong example of what’s possible when you combine Gemini models with BigQuery ML on GCP.
👉 Watch the General Keynote: Forecasting with AI Agents- https://goo.gle/4sWn5nP
Why Build AI Agents on Google Cloud?
These codelabs aren’t just tutorials they reflect how Google Cloud AI services are actually being used in production across industries.
The Google Cloud AI platform (Vertex AI) gives you:
– Access to Gemini — Google’s most capable AI model family
– Agent Builder for no-code agent creation
– Agent Engine for managed, scalable deployment
– Native integrations with BigQuery, Spanner, Maps, and Cloud Run
– Enterprise security, compliance, and observability built in
Whether you’re exploring Google Cloud AI training to upskill, building a product, or pursuing cloud AI jobs ,these labs give you the hands-on experience that matters.
