🏆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

Deploy multi-agent systems with Agent Development Kit and Agent Engine

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 Vertex AI Agent Engine to run as a managed agentic flow, with infrastructure decisions and resource scaling handled by Agent Engine.

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 Engine.
  • Query an agent app running on Agent Engine.
  • 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.
Activities

Lab: Get started with Agent Development Kit (ADK)

Lab: Empower ADK agents with 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.
Activities

Lab: Build multi-agent systems with ADK

Objectives
  • Describe the benefits of deploying agents, especially multi-agent systems, to Agent Engine over self-hosting, such as in Vertex AI online predictions.
  • Demonstrate deploying to Agent Engine.
  • Demonstrate querying a deployed agent app.
Topics covered
  • →Deploying agent apps to Agent Engine and querying responses.
Activities

Lab: Deploy ADK agents to Agent Engine

Objectives
  • Evaluate agents within the Agent Development Kit.
  • Use the web interface to view evaluations.
Topics covered
  • →Evaluate agents within the Agent Development Kit.

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Agent Operations on Google Cloud

In this course, you will use your knowledge of developing agents using the Agent Development Kit to operationalize agents using Agent Operations (AgentOps) on Google Cloud. After reviewing the challenges of managing production agents and deployment targets, you will build CI/CD pipelines for agents and leverage a governed artifact management ecosystem. You will implement evaluation systems using the GenAI Evaluation Service and ADK and apply observability solutions for debugging with Cloud Logging and Cloud Trace. You will integrate security guardrails against agent-specific threats using Model Armor and Sensitive Data Protection. Finally, you will apply FinOps strategies to understand and manage agent costs.

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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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