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Course Outline
Foundations of AI Agents on Google Cloud
- Definition of AI agents and distinctions from chatbots and standard AI applications
- Typical enterprise business use cases for agents
- Overview of Google Cloud services utilized in agent development
Designing Agent Architecture
- Essential components of an agent: model, instructions, tools, memory, and workflow
- Selecting the appropriate level of agent capability 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
- Creating a basic agent using Vertex AI and Gemini models
- Testing prompts, responses, and basic agent behavior
Connecting Agents to Tools and Data
- Enabling tool usage through APIs and function calling
- Integrating agents with business data to ensure grounded responses
- Enhancing reliability, relevance, and response quality
Deploying and Operating Agents
- Deployment strategies for agent solutions on Google Cloud
- Monitoring, logging, and basic assessment of agent performance
- Considerations for security, access control, and responsible AI
Practical Workshop and Next Steps
- Developing a simple agent for a realistic business scenario
- Evaluating design choices and identifying areas for improvement
- Planning subsequent steps for pilot projects and ongoing learning
Requirements
- Fundamental understanding of cloud computing principles and web applications
- Knowledge of APIs, JSON, and either Google Cloud services or comparable cloud platforms
- Basic programming proficiency in Python, JavaScript, or other modern languages
Target Audience
- Developers aiming to create AI agents on Google Cloud
- Technical leads and solution architects exploring agent-based applications
- Data and AI professionals seeking practical experience with Vertex AI agent capabilities
7 Hours
