Building a Multi-Agent System using Gemini and Gemma Models

Welcome to this hands-on workshop! We are moving beyond single-prompt chatbots to explore the architecture of Multi-Agent Systems (MAS). In production environments, relying on one massive, monolithic model to handle everything leads to high costs, latency, and frequent errors. The professional solution is to build a collaborative team of smaller, specialized AI workers.

In this session, you will learn how to orchestrate Gemini (as your high-level executive manager) alongside Gemma (running as specialized, task-focused local workers) to create an automated, production-grade AI system.

🏗️ Today’s Technical Roadmap

We will build an automated Course Creation System from scratch using a modular architecture:

1. The Orchestrator (The Brain): Setting up a Gemini agent. Because of its massive context window and advanced reasoning capabilities, Gemini will act as the team manager—breaking down user requests and delegating specific tasks to worker agents.

2. The Researcher Agent: Configuring a worker agent equipped with native function-calling tools to crawl Google Search, gather up-to-date documentation, and aggregate learning materials.

3. The Judge Agent (Quality Control): Creating a specialized grading agent that acts as a virtual peer reviewer, checking the research data for completeness and accuracy before passing it forward.

4. The Content Builder (The Worker Engine): Deploying a lightweight Gemma model onto a Cloud Run GPU instance (NVIDIA L4). Gemma will take the approved research data and autonomously write out the final structured markdown course files.

5. The Orchestration Loop: Linking these components using SequentialAgent and LoopAgent design patterns so the agents can seamlessly debate, self-correct errors, and execute tasks in parallel.

🌟 Why This Session is Critical

• Cost & Performance Optimization: Running enterprise workloads entirely on frontier models is expensive. You will learn how to use hybrid architectures—leveraging premium models like Gemini only for high-level logic, while routing heavy text-generation tasks to efficient open-weights models like Gemma.

• Production Patterns: This lab covers core patterns used in industry-grade agent platforms, such as loop limits (max_iterations) to prevent infinite agent loops and strict Pydantic models to eliminate data errors.

• Next-Gen Portfolio Project: Building a fully functional multi-agent coordination pipeline and deploying it serverless on Cloud Run is a definitive proof-of-work project that stands out to global engineering teams.

📝 What You Need to Bring

• Your Laptop (fully charged).

• A Google Cloud Project with billing enabled so you can provision the Cloud Run resources and access Model Garden.

📚 Essential Developer Resources

• Google Codelabs: Step-by-Step Multi-Agent Implementation Guide

• Google Cloud Architecture Center: Reference Architecture for Multi-Agent AI Systems

• Gemma Open Models: Discover and Deploy Gemma in Model Garden