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 multiagent workflows.
- Duration: 1 day
- Languages: English, German
- Level: Intermediate
- Group size: Up to 16
What you'll 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)
Course outline
Vertex AI and AI Applications
- Vertex AI for generative AI
- AI Applications: Search, Recommendations, and Agents
- Use cases for Vertex AI Search and Agents
Vertex AI Search
- Basic concepts: apps, engines, and data stores
- Data sources and preparing data
- Creating a data store
- Configuring Vertex AI Search
- Deploy Vertex AI Search
Grounding Applications
- Why is grounding important?
- Retrieval augmented generation (RAG)
- Grounding options on Google Cloud
- Testing grounding in Vertex AI Studio
- Grounding using the Vertex AI SDK
Conversational Agents
- Customer Engagement Suite (CES)
- Deterministic vs. generative agents
- Playbooks
- Data store tools
Multi-agent Applications on Vertex AI
- 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
Related courses
Agent Observability on Google Cloud
This course provides an applied, intermediate guide to operationalizing AI agents, focusing specifically on achieving production confidence and cost predictability for Gemini-powered workflows on Google Cloud. Participants will learn the methodology and actionable skills necessary to transform non-deterministic agent logic into transparent, auditable, and scalable systems.The course covers core operational disciplines, including mapping the agent's complex thought process (ReAct loops) to Cloud Trace Spans for debugging, implementing Logs-Based Security Metrics for compliance, and setting up actionable alerts and custom dashboards in Cloud Monitoring to proactively control cost overruns and quality drift. The course uses presentations, Visual Walkthroughs, and strategic discussions to ensure effective learning that is directly applicable to the Vertex AI ecosystem
AI Infrastructure Essentials
This course provides a foundational overview of the hardware, software, and networking components required to develop and manage AI models at scale. It explores Google Cloud's AI Hypercomputer architecture, compares compute accelerators like GPUs and TPUs, and examines the critical data pipelines and storage solutions necessary to maximize training performance. It is designed for IT decision-makers and infrastructure architects seeking to understand enterprise-grade AI deployment.
Gemini in BigQuery for Data Practitioners
This course demonstrates how to use AI/ML models for generative AI tasks in BigQuery. Through a practical use case involving customer relationship management, you learn the workflow of solving a business problem with Gemini models. To facilitate comprehension, the course also provides step-by-step guidance through coding solutions using both SQL queries and Python notebooks.
Not sure which course fits?
Book a 30-minute call. We'll look at where your team is, what they need to be able to do, and put together either a specific course date or a tailored learning plan.