AI+ Agent™
Empower businesses with AI + Agent ™ to design, deploy, and scale intelligent agents.Empower Automation with AI+ Agent™ for intelligent, efficient task execution Beginner-Frie...
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49 sessions
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Skills You’ll Gain
- Aspiring AI Professionals: Learners looking to break into AI by building practical experience with intelligent agents and automation.
- Software Developers & Engineers: Python or similar language programmers who want to design, integrate, and deploy AI agents into real applications.
- Data Analysts & Data Scientists: Professionals who work with data and want to operationalize insights through AI-driven agents and workflows.
- Product Managers & Tech Leaders: Decision-makers aiming to understand, plan, and oversee AI agent solutions that enhance products and services.
- Automation & Operations Specialists: Those focused on process optimization who want to replace repetitive tasks with smart, autonomous AI agents.
- Basic Understanding of AI Concepts – Familiarity with core AI principles.
- Programming Knowledge – Proficiency in Python or similar languages.
- Data Analysis Skills – Ability to interpret and manipulate datasets.
- Problem-Solving Mindset – Analytical thinking to address AI challenges.
- Familiarity with Machine Learning – Understanding basic ML algorithms and techniques.
- 1.1 Understanding AI Agents
- 1.2 Anatomy and Ecosystem of AI Agents
- 1.3 Applications, Misconceptions, and Mini Case Studies
- 1.4 Case Study: Transforming Customer Support at Acme Retail with AI Agents
- 1.5 Hands-On Exercise 1: Build a Q&A ChatBot Using Gemini + Prompt + LLM Chain in Flowise Cloud
- 2.1 Anatomy of an AI Agent
- 2.2 Classification of AI Agents
- 2.3 Matching Agents to Use Cases
- 2.4 Case Study: Enhancing Mental Health Support with AI Agents at Earkick
- 2.5 Hands-On Exercise
- 3.1 No-code and visual agent platforms
- 3.2 Tools Overview and Setup
- 3.3 Start building: “Your First Flow” with n8n
- 3.4 Case Study: Empowering HR with AI – Building an Onboarding Assistant Without Coding
- 3.5 Hands-on Exercise
- 4.1 Agent 1
- 4.2 Agent 2
- 4.3 Agent 3
- 4.4 Agent 4
- 4.5 Troubleshooting and Validation of AI Agents
- 4.6 Share Your AI Agent
- 4.7 Hands-On Exercise 1
- 5.1 Multi-Tool Agents
- 5.2 Agent Chaining and Workflow Basics
- 5.3 Managing Agent State: State, Context, and User Journey
- 5.4 Prompt Engineering for Agents
- 5.5 Multi-Agent Systems (MAS)
- 5.6 Case Study: Smarter Marketing Campaigns with Tool Chaining
- 5.7 Hands-on Exercise: Automating Order Tracking and Notifications with Make.com
- 6.1 Deploying Agents
- 6.2 Channel Selection – Where the User will Interact
- 6.3 Hosting Environment – Where does the Agent Run?
- 6.4 Data Integration
- 6.5 Security Setup
- 6.6 Monitoring & Updates
- 6.7 Application Mapping
- 6.8 Hands-on Exercise 1: Integration of a Portfolio Assistant Chatbot into GitHub Pages using Zapier
- 7.1 Observability Basics
- 7.2 Performance Evaluation: Key Metrics
- 7.3 Guardrails: Preventing Misuse & Ensuring Safe Outputs
- 7.4 Responsible AI
- 7.5 Mini-Case: Failure and Recovery in Agent Deployments
- 7.6 Real-world Failures
- 7.7 Peer Sharing: How to Present and Discuss Agent Logs/Results
- 8.1 Capstone Project 1: Smart Personal AI Assistant
- 8.2 Capstone Project 2: Smart Lead Engagement – From Email to Personalized Outreach – Sales Support Agent
- 8.3 Capstone Project 3: Education Tutor Agent
- 8.4 HR Knowledge Bot
- 8.5 Customer Service Agent
- 8.6 Healthcare Triage Bot
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