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Course Outline
Basics of AI Agents on Google Cloud
- Definition of AI agents and their distinction from chatbots and conventional AI applications
- Typical enterprise use cases for agents in business environments
- Introduction to the Google Cloud services utilized in agent development
Architecting Agent Solutions
- Key elements 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 foundational agent
- Testing prompts, responses, and basic agent behavior
Integrating Agents with Tools and Data
- Enabling tool usage through APIs and function calling
- Linking the agent to business data to ensure grounded responses
- Enhancing reliability, relevance, and the quality of responses
Deployment and Operations of Agents
- Available deployment methods for agent solutions on Google Cloud
- Monitoring, logging, and assessing agent performance
- Considerations for security, access control, and responsible AI practices
Practical Workshop and Future Planning
- Constructing a simple agent for a realistic business scenario
- Evaluating design decisions and identifying areas for improvement
- Outlining next steps for pilot projects and continued learning
Requirements
- Fundamental knowledge of cloud computing concepts and web applications
- Experience with APIs, JSON, and Google Cloud services or equivalent cloud platforms
- Basic proficiency in Python, JavaScript, or another modern programming 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 specialists looking for practical experience with Vertex AI agent features
7 Hours
