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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
- Typical business use cases for agents in enterprise settings
- Summary of Google Cloud services utilized in agent development
Designing Agent Architecture
- Key components of an agent: model, instructions, tools, memory, and workflow
- Selecting the appropriate agent capability level for a given business scenario
- Composing effective instructions and establishing basic guardrails
Building an Agent with Vertex AI and Gemini
- Setting up the Google Cloud environment for development
- Creating a basic agent using Vertex AI and Gemini models
- Testing prompts, responses, and fundamental agent behavior
Connecting Agents to Tools and Data
- Facilitating tool usage through APIs and function calling
- Linking the agent to business data to ensure grounded responses
- Enhancing reliability, relevance, and overall response quality
Deploying and Operating Agents
- Deployment strategies for agent solutions on Google Cloud
- Monitoring, logging, and basic performance evaluation of agents
- Addressing security, access control, and responsible AI principles
Practical Workshop and Next Steps
- Constructing a simple agent for a realistic business scenario
- Evaluating design decisions and potential 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 proficiency in Python, JavaScript, or another contemporary language
Audience
- Developers aiming to create AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based applications
- Data and AI practitioners seeking practical experience with Vertex AI agent capabilities
7 Hours
