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Course Outline
Foundations of AI Agents on Google Cloud
- Defining AI agents and distinguishing them from chatbots and standard AI applications.
- Exploring common business use cases for agents in enterprise settings.
- Reviewing the Google Cloud services utilized in agent development.
Designing Agent Architecture
- Identifying core agent components: model, instructions, tools, memory, and workflow.
- Selecting the appropriate level of agent capability for a given business scenario.
- Crafting effective instructions and establishing basic guardrails.
Building an Agent with Vertex AI and Gemini
- Configuring 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
- Implementing 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
- Evaluating deployment options for agent solutions on Google Cloud.
- Monitoring, logging, and performing basic evaluations of agent performance.
- Addressing security, access control, and responsible AI considerations.
Practical Workshop and Future Steps
- Developing a simple agent for a realistic business use case.
- Analyzing design choices and identifying areas for improvement.
- Planning next steps for pilot projects and continued learning.
Requirements
- A foundational understanding of cloud computing concepts and web applications.
- Familiarity with APIs, JSON, and Google Cloud services or comparable cloud platforms.
- Basic programming proficiency in Python, JavaScript, or another modern language.
Target Audience
- Developers aiming to build AI agents on Google Cloud.
- Technical leads and solution architects investigating agent-based applications.
- Data and AI practitioners seeking practical experience with Vertex AI agent capabilities.
7 Hours
