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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 use cases for agents in enterprise settings
- An 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 level of agent capability 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
Connecting Agents to Tools and Data
- Enabling tool utilization through APIs and function calling
- Linking the agent to business data to ensure grounded responses
- Enhancing reliability, relevance, and the quality of responses
Deploying and Operating Agents
- Deployment strategies for agent solutions on Google Cloud
- Monitoring, logging, and basic assessment of agent performance
- Security, access control, and responsible AI considerations
Practical Workshop and Future Steps
- Constructing a simple agent for a realistic business scenario
- Evaluating design decisions and identifying areas for improvement
- Planning next steps for pilot projects and continued learning
Requirements
- Fundamental knowledge of cloud computing principles and web applications
- Proficiency with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Basic programming skills in Python, JavaScript, or another contemporary language
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
