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Course Outline
Foundations of AI Agents on Google Cloud
- Understanding what AI agents are and how they distinct themselves from chatbots and conventional AI applications
- Typical business use cases for agents in enterprise settings
- Overview of the Google Cloud services employed 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 fundamental 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 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 preliminary assessment of agent performance
- Security, access control, and responsible AI considerations
Practical Workshop and Future Steps
- Developing a straightforward agent for a realistic business application
- Reviewing design decisions and identifying areas for improvement
- Planning next steps for pilot projects and ongoing learning
Requirements
- A foundational grasp of cloud computing concepts and web applications
- Knowledge of APIs, JSON, and Google Cloud services or comparable cloud platforms
- Foundational programming experience in Python, JavaScript, or other contemporary languages
Target Audience
- Developers seeking to create AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based applications
- Data and AI specialists aiming for hands-on experience with Vertex AI agent capabilities
7 Hours
