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Course Outline
Foundations of AI Agents on Google Cloud
- Definition of AI agents and their distinction from chatbots and standard AI applications
- Typical business use cases for agents in enterprise settings
- Overview of 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 specific business scenarios
- Crafting effective instructions and establishing basic guardrails
Building an Agent with Vertex AI and Gemini
- Setting up the Google Cloud environment for development
- Using Vertex AI and Gemini models to create a foundational agent
- Testing prompts, responses, and basic agent behaviors
Connecting Agents to Tools and Data
- Facilitating tool usage through APIs and function calling
- Linking the agent to business data for grounded responses
- Enhancing reliability, relevance, and response quality
Deploying and Operating Agents
- Deployment strategies for agent solutions on Google Cloud
- Monitoring, logging, and preliminary evaluation of agent performance
- Security, access control, and responsible AI considerations
Practical Workshop and Next 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 fundamental grasp of cloud computing concepts and web applications
- Familiarity with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Basic programming experience in Python, JavaScript, or another contemporary language
Target Audience
- Developers seeking to build 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
