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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
- Typical business applications for agents in enterprise settings
- 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 agent capability level for specific business scenarios
- Crafting effective instructions and establishing basic guardrails
Developing an Agent with Vertex AI and Gemini
- Setting up the Google Cloud environment for development
- Utilizing Vertex AI and Gemini models to create a basic agent
- Testing 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 Directions
- Constructing a simple agent for a realistic business use case
- Evaluating design decisions and identifying areas for improvement
- Planning next steps for pilot projects and continued learning
Requirements
- A foundational understanding of cloud computing principles and web applications
- Knowledge of APIs, JSON, and Google Cloud services or equivalent cloud platforms
- Basic programming proficiency in Python, JavaScript, or other modern languages
Target Audience
- Developers seeking to construct AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based applications
- Data and AI professionals looking for practical experience with Vertex AI agent capabilities
7 Hours
