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Course Outline
Foundations of AI Agents on Google Cloud
- Defining AI agents and distinguishing them from chatbots and conventional AI applications
- Identifying prevalent enterprise business use cases for agents
- Surveying the Google Cloud services employed in agent development
Designing Agent Architecture
- Understanding core agent components: model, instructions, tools, memory, and workflow
- Selecting appropriate agent capability levels 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 foundational agent
- Evaluating prompts, responses, and basic agent behaviors
Connecting Agents to Tools and Data
- Facilitating 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 performance evaluation
- Addressing security, access control, and responsible AI considerations
Practical Workshop and Next Steps
- Constructing a simple agent for a realistic business scenario
- Assessing design decisions and identifying areas for improvement
- Outlining subsequent steps for pilot projects and continued learning
Requirements
- Fundamental knowledge of cloud computing concepts and web applications
- Proficiency with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Elementary programming skills in Python, JavaScript, or another contemporary language
Target Audience
- Developers aiming to create AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based applications
- Data and AI professionals seeking practical experience with Vertex AI agent capabilities
7 Hours
