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Course Outline
Foundations of AI Agents on Google Cloud
- Definition of AI agents and their distinctions from chatbots and conventional AI applications
- Typical enterprise use cases for agent deployment
- Summary of Google Cloud services involved in agent development
Architecting AI Agents
- Essential agent components: models, instructions, tools, memory, and workflow structures
- Selecting the appropriate agent capability level for specific business contexts
- Formulating effective instructions and establishing basic guardrails
Developing Agents with Vertex AI and Gemini
- Setting up the Google Cloud environment for development tasks
- Utilizing Vertex AI and Gemini models to construct a foundational agent
- Evaluating prompts, responses, and basic agent interactions
Integrating Agents with Tools and Data
- Facilitating tool utilization via APIs and function calling
- Linking agents to business data to ensure grounded responses
- Enhancing reliability, relevance, and overall response quality
Deploying and Managing Agents
- Deployment strategies for agent solutions on Google Cloud
- Performance monitoring, logging, and basic evaluation techniques
- Security protocols, access control, and responsible AI considerations
Practical Workshop and Future Directions
- Constructing a simple agent for a realistic business scenario
- Analyzing design decisions and identifying areas for improvement
- Planning subsequent steps for pilot initiatives and continued professional development
Requirements
- Foundational knowledge of cloud computing concepts and web applications
- Experience with APIs, JSON, and Google Cloud services or comparable cloud platforms
- Basic programming proficiency in Python, JavaScript, or another contemporary language
Target Audience
- Developers seeking to create AI agents on Google Cloud
- Technical leads and solution architects investigating agent-based solutions
- Data and AI professionals aiming to gain hands-on experience with Vertex AI agent features
7 Hours
