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Course Outline
Foundations of AI Agents in Google Cloud
- Defining AI agents and distinguishing them from chatbots and standard AI applications
- Typical enterprise business use cases for agent implementation
- An overview of the Google Cloud services employed in agent development
Architectural Design for Agents
- Essential 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 Agents 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 behaviors
Integrating Agents with 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
Deployment and Operations of Agents
- Available deployment options for agent solutions on Google Cloud
- Implementing monitoring, logging, and basic performance evaluation
- Addressing security, access control, and responsible AI considerations
Hands-On Workshop and Future Directions
- Constructing a simple agent for a realistic business use case
- Evaluating design decisions and identifying areas for improvement
- Planning subsequent steps for pilot projects and continued learning
Requirements
- A foundational grasp of cloud computing principles and web applications
- Knowledge of APIs, JSON, and experience with Google Cloud services or comparable cloud platforms
- Basic programming proficiency in Python, JavaScript, or another contemporary language
Target Audience
- Developers seeking to create AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based application architectures
- Data and AI practitioners aiming to gain practical experience with Vertex AI's agent capabilities
7 Hours
