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Course Outline
Foundations of AI Agents on Google Cloud
- What AI agents are and how they differ from chatbots and standard AI applications
- Common business use cases for agents in enterprise environments
- Overview of Google Cloud services used in agent development
Designing Agent Architecture
- Core components of an agent: model, instructions, tools, memory, and workflow
- Choosing the right level of agent capability for a business scenario
- Writing effective instructions and setting basic guardrails
Building an Agent with Vertex AI and Gemini
- Preparing the Google Cloud environment for development
- Using Vertex AI and Gemini models to create a basic agent
- Testing prompts, responses, and simple agent behavior
Connecting Agents to Tools and Data
- Enabling tool use with APIs and function calling
- Connecting the agent to business data for grounded responses
- Improving reliability, relevance, and response quality
Deploying and Operating Agents
- Deployment options for agent solutions on Google Cloud
- Monitoring, logging, and basic evaluation of agent performance
- Security, access control, and responsible AI considerations
Practical Workshop and Next Steps
- Building a simple agent for a realistic business use case
- Reviewing design choices and improvement opportunities
- Planning next steps for pilot projects and further learning
Requirements
- A basic understanding of cloud computing concepts and web applications
- Familiarity with APIs, JSON, and Google Cloud services or a similar cloud platform
- Basic programming experience in Python, JavaScript, or another modern language
Audience
- Developers who want to build AI agents on Google Cloud
- Technical leads and solution architects exploring agent-based applications
- Data and AI practitioners who want practical experience with Vertex AI agent capabilities
7 Hours
