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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.
- Identifying common enterprise use cases for agent deployment.
- An overview of the Google Cloud services relevant to agent development.
Designing Agent Architecture
- Core agent components: models, instructions, tools, memory, and workflow logic.
- Selecting the appropriate level of agent capability for specific business scenarios.
- Drafting effective instructions and establishing basic guardrails.
Building an Agent with Vertex AI and Gemini
- Setting up the Google Cloud environment for development.
- Utilising Vertex AI and Gemini models to create a basic agent.
- Testing prompts, responses, and foundational agent behaviour.
Connecting Agents to Tools and Data
- Enabling tool usage through APIs and function calling.
- Linking agents to business data to ensure grounded, accurate responses.
- Enhancing reliability, relevance, and overall response quality.
Deploying and Operating Agents
- Reviewing deployment options for agent solutions on Google Cloud.
- Implementing monitoring, logging, and basic performance evaluation.
- Addressing security, access control, and responsible AI considerations.
Practical Workshop and Next Steps
- Constructing a simple agent for a realistic business scenario.
- Reviewing design decisions and identifying opportunities for improvement.
- Planning next steps for pilot projects and continued learning.
Requirements
- A foundational grasp of cloud computing principles and web applications.
- Proficiency with APIs, JSON, and Google Cloud services or comparable cloud platforms.
- Basic programming skills in Python, JavaScript, or another contemporary language.
Audience
- Developers aiming to build AI agents on the Google Cloud platform.
- Technical leads and solution architects exploring agent-based application architectures.
- Data and AI professionals seeking hands-on experience with Vertex AI agent capabilities.
7 Hours
