Building Smart Agents with Vertex AI Agent Builder & RAG Training Course
Vertex AI Agent Builder is a no-code/low-code environment for creating grounded agents that combine generative models with retrieval-augmented generation (RAG), allowing teams to rapidly build agents that use enterprise data and search to provide accurate, context-aware responses.
This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to design, configure, and deploy smart agents using Vertex AI Agent Builder and RAG patterns.
By the end of this training, participants will be able to:
- Design grounded agent workflows using Agent Builder.
- Implement RAG pipelines with search and vector stores.
- Integrate enterprise data sources securely for retrieval.
- Evaluate and iterate agent behavior using testing and metrics.
Format of the Course
- Interactive lecture and discussion.
- Hands-on labs using Vertex AI Agent Builder and RAG components.
- Project-based exercises to build and refine agents.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Agent Builder and RAG
- Overview of Agent Builder capabilities
- RAG fundamentals and when to use them
- Use cases and success stories
Setting Up the Environment
- Configuring Vertex AI workspace
- Connecting search and vector stores
- Hands-on lab: environment preparation
Designing Grounded Agent Workflows
- Defining agent goals and conversation flows
- Mapping data sources to retrieval strategies
- Hands-on lab: building a conversation flow
Implementing RAG Pipelines
- Indexing documents and embeddings
- Retriever and re-ranker patterns
- Hands-on lab: creating a RAG pipeline
Integrations and Enterprise Data
- Secure connectors to internal systems
- Data governance and access controls
- Hands-on lab: connecting enterprise data sources
Testing, Evaluation, and Iteration
- Prompt testing and evaluation metrics
- User simulation and validation strategies
- Hands-on lab: evaluating and tuning the agent
Deployment, Monitoring, and Maintenance
- Deployment options and scaling considerations
- Monitoring performance, relevance, and drift
- Operational playbooks for updates and rollback
Summary and Next Steps
Requirements
- Basic knowledge of natural language processing
- Experience with cloud services and APIs
- Familiarity with search and vector databases
Audience
- Developers
- Solution architects
- Product managers
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