Course Objective
To provide the engineering skills required to build end-to-end autonomous or semi-autonomous agent systems on Google Cloud using Vertex AI Agent Engine and the Agent Development Kit (ADK), running them on a cloud-scale managed runtime.
Participants learn how to transition local reasoning engine prototypes into production environments with enterprise security, session management, tool calling, and high-availability standards.
Target Audience and Prerequisites
- Target Audience: Cloud AI engineers, software architects, platform engineers, and technical teams building enterprise agent solutions on Google Cloud.
- Prerequisites: Intermediate Python programming skills, familiarity with core Google Cloud concepts (IAM, Cloud Run, Vertex AI), and experience using LLM APIs.
Key Takeaways and Focus Areas
- Building modular single-agent and
multi-agentworkflows with the Agent Development Kit (ADK). - Configuring
stateful sessionmanagement,memory, and secure code execution runtimes on Vertex AI Agent Engine. - Integrating enterprise data sources, Apigee API layers, and Google Cloud services via secure
toolandgroundingmechanisms.
Curriculum
Vertex AI Agent Engine Architecture and ADK Fundamentals
- Managed agent runtime architecture and Reasoning Engine components on Google Cloud
- Designing deterministic and dynamic agent workflows with the Agent Development Kit (ADK)
Tool Calling, Grounding, and Enterprise Data Connections
- Function declarations, OpenAPI schemas, and enterprise grounding with Vertex AI Search
- Secure integration with external systems via BigQuery, Cloud SQL, and Apigee
Session Management, Memory, and Multi-Agent Orchestration
- Short-term and long-term memory management with stateful session continuity
- Running hierarchical and collaborative multi-agent topologies on Agent Engine
Deployment, Scaling, and Operational Security
- Agent packaging, versioning, CI/CD pipelines, and autoscaling configurations
- VPC Service Controls, IAM permissions, and runtime isolation
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