課程資料
時間表
- 2026年7月5日(週日) - 2026年8月2日(週日) 上午 10:00 - 下午 1:00
報名日期
2026年6月15日(週一) - 2026年7月4日(週六)
價錢
HKD 6,000
課程級別
學習模式
修業期
15 小時
教學語言
廣東話
地點
長沙灣分校
證書
- 可獲發
- *證書的發放與分配，依課程提供者的政策及安排而定。
課程簡介
I Agents 智能代理是利用人工智慧獨立執行任務的智能軟體程式。它們運用大型語言模型（LLMs）等先進技術，處理資訊、做出決策並與用戶或系統互動。例如，AI智能代理可驅動聊天機器人回答客戶問題、分析行銷數據或自動化醫療和零售等行業的日常任務。
這些代理使用 Python、Zapier 或 LangChain 等工具構建，能理解自然語言、適應多種場景，並與 Microsoft Azure 等雲端平台整合以實現可擴展性。
透過自動化工作流程和提供個人化解決方案，AI Agents 智能代理提升效率並推動創新，成為數位時代企業不可或缺的工具。
它們的學習和適應能力確保能有效滿足多樣化需求。
課程內容
Module 1: Foundations of AI Agents
Introduction to AI Agents: Core principles, architecture, and operational mechanisms
Prompt Engineering: Advanced techniques for optimizing AI model interactions
Applications and Frameworks: Analysis of prevalent use cases and solution design methodologies
Development Environment Setup: Configuration of essential tools and platforms for AI agent development
Module 2: Designing a Customer Support AI Chatbot
Chatbot Development: Constructing an intelligent chatbot using Zapier, n8n, Python, and large language models (LLMs)
Core Functionality: Implementing natural language processing for effective customer query resolution
Practical Implementation: Step-by-step guidance on building and testing a responsive chatbot system
Module 3: Building a Marketing Analytics Agent
Automated Analytics System: Developing a robust marketing analytics engine
Data Acquisition and Analysis: Fetching real-time news data and performing sentiment analysis
Automated Reporting: Summarizing insights and automating email-based report distribution
Integration Tools: Leveraging Zapier, n8n, and Python for seamless workflow automation
Module 4: Advanced AI Agent Development
Sophisticated AI Solutions: Utilizing LangChain, Zapier, and Python to create scalable and resilient AI agents
Advanced Methodologies: Enhancing agent performance through complex workflows and integrations
Scalable Design: Architecting agents to support diverse and evolving business requirements
Module 5: Production Deployment Strategies
Deployment Fundamentals: Key considerations for deploying AI agents in production environments
Cloud-Based Deployment: Configuring and deploying solutions on cloud platforms, such as Microsoft Azure
Operational Best Practices: Ensuring reliability, scalability, and security in live deployments
