🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
GCP300ADKAE

Multi-Agent Systems with ADK and Agent Runtime

In this course, you'll learn to use the Google Agent Development Kit to build complex, multi-agent systems. You will build agents equipped with tools, and connect them with parent-child relationships and flows to define how they interact. You'll run your agents locally and deploy them to Agent Runtime to run as a managed agentic flow, with infrastructure decisions and resource scaling handled by Agent Runtime.

Google Cloud
✓ Official training Google CloudLevel Advanced⏱️ 1 day (7h)

What you will learn

  • Build an agent with tools using the Google Agent Development Kit.
  • Establish interaction patterns between multiple agents with parent-child relationships and flows.
  • Utilize features such as session memory, artifact storage, and callbacks.
  • Deploy a multi-agent app to Agent Runtime.
  • Query an agent app running on Agent Runtime.
  • Evaluate agents within the Agent Development Kit.

Prerequisites

  • Python
  • gen AI prompt engineering
  • gen AI tool use

Target audience

  • Machine learning engineers, Gen AI engineers

Training Program

5 modules to master the fundamentals

Objectives
  • Explain how the Agent Development Kit compares to other tools such as the Google Gen AI SDK or LangChain.
  • Describe the parameters used to build an agent in Agent Development Kit.
Topics covered
  • →Basics of building an agent in the Agent Development Kit.
Objectives
  • Discuss the importance of structured docstrings and typing when writing tool functions for agents.
  • Demonstrate the ability to provide tools to an agent.
  • List common and useful tools available for the Agent Development Kit agents, including LangChain tools.
Topics covered
  • →Enhance agents with tools and cover the growing breadth of available tools.
Objectives
  • Describe the directory structure and naming conventions encouraged by the Agent Development Kit.
  • Demonstrate the ability to create multiple agents and relate them to one another with parent-child relationships.
  • Describe the different flow options and when you might use them.
  • Get responses that have passed through multiple agents.
  • Control content at different points with callbacks.
Topics covered
  • →Manage communication and task-sharing between agents through parent-child relationships and flows to enable coordinated responses to queries.
Objectives
  • Describe the benefits of deploying agents, especially multi-agent systems, to Agent Runtime over self-hosting, such as in Agent Platform online predictions.
  • Demonstrate deploying to Agent Runtime.
  • Demonstrate querying a deployed agent app.
Topics covered
  • →Deploying agent apps to Agent Runtime and querying responses.
Objectives
  • Run programmatic evaluation benchmarks on multi-agent workflows using the Agent Development Kit
  • Inspect and analyze evaluation metrics utilizing the diagnostic web interface
Topics covered
  • →Evaluate agents within the Agent Development Kit.

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Upcoming sessions

October 8, 2026
Distanciel • Français
Register

Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used
  • Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
  • Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
  • Guided Labs — Guided practical exercises on software, hardware, or technical environments.
  • Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

  • Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
  • Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
  • Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.

790€ excl. VAT

per learner

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