Most AI demos today focus on a single chatbot answering questions. But real-world problems are rarely solved by one “smart” assistant.

In this session, we’ll explore how multiple AI agents can work together like a real team — each with a clear role — to solve complex tasks more reliably. Just like humans, some agents research, some review, some create content, and one coordinates the entire process.

Using a practical example of an AI-powered course creation system, you’ll see how agents can collaborate, review each other’s work, fix mistakes, and produce better results than a single AI model acting alone.

We’ll walk through building a distributed multi-agent system using Google’s Agent Development Kit (ADK) and the Agent-to-Agent (A2A) protocol.

The system is composed of specialized agents:

A Researcher Agent that uses web search tools to gather up-to-date information
A Judge Agent that evaluates research quality using structured outputs (Pydantic)
A Content Builder Agent that turns validated research into structured content
An Orchestrator Agent that manages control flow and agent collaboration
You’ll see how LoopAgent enables self-correction through feedback loops, how SequentialAgent orchestrates higher-level workflows, and how agents communicate over A2A instead of relying on a single monolithic prompt.

We’ll also cover how to run the system locally and deploy it to Google Cloud Run, making the architecture production-ready and scalable.

This session is targeted at developers and architects with basic familiarity with large language models and cloud-native applications. No prior experience with multi-agent frameworks is required. The session is designed as a 30–45 minute talk with optional demo segments. Code examples are simplified for clarity and focus on architecture and orchestration patterns. This session has not been previously delivered publicly.