🏆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
GCP200SEARCHAGENTS

Develop Search and Agents with AI Applications

In this course, you learn how to use Vertex AI Search and Conversational Agents to create search engines and chat applications. You will learn how to leverage Vertex AI Search for grounding your gen AI-powered applications. You will then explore how to integrate these search engines and chat applications into your own applications. Finally, you learn how to use the agents built in AI Applications in multi-agent workflows.

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

What you will learn

  • Explore Vertex AI as a platform for enterprise-ready generative AI.
  • Create and deploy a search engine using Vertex AI Search.
  • Ground outputs from foundation models using Vertex AI Search.
  • Create a chat application using natural language in Conversational Agents.
  • Integrate your agents into multi-agent workflows using Agent Development Kit and Vertex AI Agent Builder.

Prerequisites

  • Knowledge of generative AI basic concepts and programming knowledge (Python)

Target audience

  • Customers

Training Program

5 modules to master the fundamentals

Objectives
  • Explore the options in Vertex AI for generative AI.
  • Identify the role of AI Applications: Search, Recommendations, and Agents.
  • Examine use cases for Vertex AI Search and Agents.
Topics covered
  • →Vertex AI for generative AI
  • →AI Applications: Search, Recommendations, and Agents
  • →Use cases for Vertex AI Search and Agents
Objectives
  • Create enterprise-grade generative AI applications with AI Applications.
  • Choose the appropriate engine for a search or conversation app.
  • Import data into a data store.
  • Create and configure a custom search app.
Topics covered
  • →Basic concepts: apps, engines, and data stores
  • →Data sources and preparing data
  • →Creating a data store
  • →Configuring Vertex AI Search
  • →Deploy Vertex AI Search
Activities

Lab: Integrating Vertex AI Search Into Your Application

Objectives
  • Identify why grounding is important.
  • Leverage Retrieval-Augmented Generation (RAG).
  • Use grounding options on Google Cloud.
  • Test grounding using the Vertex AI Studio and SDK.
Topics covered
  • →Why is grounding important?
  • →Retrieval augmented generation (RAG)
Activities

Lab: Grounding LLMs with Vertex AI Search

Objectives
  • Create conversational agents.
  • Manage conversations with playbooks.
  • Use examples to improve a playbook's response.
  • Leverage data store tools to perform grounding for your playbook's responses.
Topics covered
  • →Customer Engagement Suite (CES)
  • →Deterministic vs. generative agents
  • →Playbooks
  • →Data store tools
Activities

Lab: Create a Conversational Agent Playbook that connects to an unstructured data store tool

Objectives
  • Explore the features and benefits of the Agent Development Kit (ADK).
  • Accelerate agent development with Agent Garden.
  • Use ADK to build multi-agent applications.
  • Leverage a Data Store tool from an ADK agent.
Topics covered
  • →Multi-agent applications and agentic AI
  • →Introduction to Agent Developer Kit (ADK)
  • →Vertex AI Agent Garden
  • →Vertex AI Agent Engine
  • →Putting it all together with AI Applications
Activities

Lab: Using ADK and multiple agents with AI Applications

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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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