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Course Outline
Essentials of AI Agents on Google Cloud
- Defining AI agents and distinguishing them from chatbots and traditional AI applications
- Typical enterprise use cases for agent implementation
- Summary of Google Cloud services utilized in agent development
Architectural Design for Agents
- Key agent components: models, instructions, tools, memory, and workflows
- Selecting appropriate agent capabilities for specific business contexts
- Crafting effective instructions and establishing initial guardrails
Agent Development with Vertex AI and Gemini
- Setting up the Google Cloud environment for development
- Creating a foundational agent using Vertex AI and Gemini models
- Testing prompts, responses, and basic behavioral patterns
Integrating Agents with Tools and Data
- Facilitating tool usage via APIs and function calling
- Linking agents to business data to ensure grounded responses
- Enhancing reliability, relevance, and overall response quality
Deployment and Agent Operations
- Available deployment pathways for agent solutions on Google Cloud
- Performance monitoring, logging, and foundational evaluation strategies
- Security protocols, access management, and responsible AI practices
Practical Workshop and Future Directions
- Developing a simple agent for a realistic business scenario
- Analyzing design decisions and identifying areas for improvement
- Strategizing next steps for pilot initiatives and continued learning
Requirements
- Foundational knowledge of cloud computing principles and web-based applications
- Proficiency with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Basic programming competency in Python, JavaScript, or another contemporary language
Target Audience
- Developers seeking to implement AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based application architectures
- Data and AI specialists aiming to gain practical experience with Vertex AI’s agent features
7 Hours
