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Course Outline
Foundations of AI Agents on Google Cloud
- Understanding what AI agents are and how they distinguish themselves from chatbots and standard AI applications
- Common enterprise use cases for agents in business environments
- An overview 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 level of agent capability for specific business scenarios
- Composing effective instructions and establishing basic guardrails
Creating Agents with Vertex AI and Gemini
- Setting up the Google Cloud environment for development
- Utilizing Vertex AI and Gemini models to develop a foundational agent
- Evaluating prompts, responses, and basic agent behavior
Integrating Agents with 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 Managing Agents
- Deployment strategies for agent solutions on Google Cloud
- Monitoring, logging, and initial evaluation of agent performance
- Security, access control, and responsible AI considerations
Practical Workshop and Future Steps
- Constructing a simple agent for a realistic business scenario
- Analyzing design decisions and potential areas for improvement
- Planning subsequent steps for pilot projects and continued learning
Requirements
- Basic knowledge of cloud computing concepts and web applications
- Experience with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Foundational programming skills in Python, JavaScript, or another modern language
Target Audience
- Developers seeking to construct AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based applications
- Data and AI professionals interested in gaining practical experience with Vertex AI agent capabilities
7 Hours
