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Course Outline
Foundations of AI Agents on Google Cloud
- Defining AI agents and distinguishing them from chatbots and standard AI applications
- Identifying common business use cases for agents in enterprise settings
- Reviewing the Google Cloud services utilized in agent development
Designing Agent Architecture
- Understanding core agent components: model, instructions, tools, memory, and workflow
- Selecting the appropriate agent capability level for specific business scenarios
- Crafting effective instructions and establishing basic guardrails
Building an Agent with Vertex AI and Gemini
- Configuring the Google Cloud environment for development
- Utilizing Vertex AI and Gemini models to construct a basic agent
- Testing prompts, responses, and fundamental agent behavior
Connecting Agents to Tools and Data
- Enabling tool usage through APIs and function calling
- Linking agents to business data to ensure grounded responses
- Enhancing reliability, relevance, and response quality
Deploying and Operating Agents
- Exploring deployment options for agent solutions on Google Cloud
- Implementing monitoring, logging, and basic evaluation of agent performance
- Addressing security, access control, and responsible AI considerations
Practical Workshop and Next Steps
- Constructing a simple agent for a realistic business use case
- Reviewing design decisions and identifying areas for improvement
- Planning next steps for pilot projects and continued learning
Requirements
- A foundational understanding of cloud computing concepts and web applications
- Familiarity with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Basic programming experience in Python, JavaScript, or another modern language
Audience
- Developers seeking to build AI agents on Google Cloud
- Technical leads and solution architects exploring agent-based applications
- Data and AI practitioners looking to gain practical experience with Vertex AI agent capabilities
7 Hours
