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.

✓ Formation officielle Google CloudNiveau Advanced⏱️ 1 jour (7h)

Ce que vous allez apprendre

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.

Prérequis

Python
gen AI prompt engineering
gen AI tool use

Public cible

Machine learning engineers, Gen AI engineers

Programme de la Formation

5 modules pour maîtriser les fondamentaux

Objectifs

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.

Sujets abordés

→Basics of building an agent in the Agent Development Kit.

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.