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Course Outline
Foundations of AI Agents on Google Cloud
- Understanding what AI agents are and how they differ from chatbots and standard AI applications
- Typical business use cases for agents in enterprise settings
- An overview of the Google Cloud services utilized in agent development
Designing Agent Architecture
- Key components of an agent: the model, instructions, tools, memory, and workflow
- Selecting the appropriate level of agent capability for a specific business scenario
- Formulating effective instructions and establishing basic guardrails
Constructing an Agent with Vertex AI and Gemini
- Setting up the Google Cloud environment for development
- Employing Vertex AI and Gemini models to create a basic agent
- Testing prompts, responses, and fundamental agent behaviors
Linking Agents to Tools and Data
- Facilitating tool usage through APIs and function calling
- Connecting the agent 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 basic evaluation of agent performance
- Security, access control, and responsible AI considerations
Practical Workshop and Future Steps
- Developing 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 understanding of cloud computing principles and web applications
- Familiarity with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Basic programming proficiency in Python, JavaScript, or another modern programming language
Target Audience
- Developers aiming to build AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based applications
- Data and AI professionals seeking practical experience with Vertex AI’s agent capabilities
7 Hours
