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Course Outline
Foundations of AI Agents on Google Cloud
- The nature of AI agents 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
- Essential components of an agent: model, instructions, tools, memory, and workflow
- Selecting the appropriate level of agent capability for a specific business scenario
- Crafting effective instructions and establishing basic guardrails
Building an Agent 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 basic agent behavior
Connecting Agents to Tools and Data
- Facilitating tool usage via APIs and function calling
- Linking the agent to business data to ensure grounded responses
- Enhancing reliability, relevance, and response quality
Deploying and Operating Agents
- Deployment alternatives for agent solutions on Google Cloud
- Monitoring, logging, and basic evaluation of agent performance
- Security, access control, and responsible AI considerations
Practical Workshop and Next Steps
- Constructing a simple agent for a realistic business scenario
- Evaluating design choices and potential improvements
- Outlining next steps for pilot projects and continued learning
Requirements
- Foundational knowledge of cloud computing principles and web applications
- Knowledge of APIs, JSON, and Google Cloud services or comparable cloud platforms
- Basic programming proficiency in Python, JavaScript, or another contemporary language
Intended Audience
- Developers seeking to develop AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based applications
- Data and AI professionals looking to gain practical experience with Vertex AI agent features
7 Hours
