Agentic AI for Product Innovation: A 4-Hour Crash Course
Unlock the power of Agentic AI to revolutionize your product innovation process in this concise, hands-on course.
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Understanding Agentic AI Fundamentals
Unit 1: Defining Agentic AI
Hello, Agentic AI!
Autonomy Explained
Proactiveness Defined
Goal-Oriented Agents
Key Characteristics
Unit 2: Agentic AI vs. Traditional AI
Traditional AI
Architectural Differences
Functional Differences
Use Cases
Unit 3: Core Components of Agentic AI
Perception
Planning
Action
Reflection
Putting It All Together
Unit 4: Agent Architectures
Symbolic Agents
Hybrid Agents
Identifying Agentic AI Opportunities in Product Innovation
Unit 1: Agentic AI in the Product Lifecycle
Product Lifecycle Stages
AI for Market Research
AI for Ideation & Concept
AI for Prototyping
AI for Testing & Iteration
Unit 2: Case Studies and Real-World Examples
Case: AI Market Research
Case: AI for Ideation
Case: AI for Prototyping
Case: AI User Feedback
Unit 3: Automating Tasks and Prioritizing Challenges
Automate Data Analysis
AI for Trend Forecasting
AI for User Feedback
Prioritizing AI Solutions
Designing Agentic AI-Powered Product Features
Unit 1: Translating Requirements into Agentic AI Specs
From Idea to AI Spec
Defining Agent Goals
Specifying Agent Actions
Data Requirements
Metrics for Success
Unit 2: Designing User Interactions
Transparency is Key
User Control
Feedback Mechanisms
Handling Errors Gracefully
Setting Expectations
Unit 3: Integrating AI into Product Architectures
API Integration
Microservices Approach
Event-Driven Architectures
Hybrid Architectures
Unit 4: Managing Complexity and Uncertainty
Monitoring Agent Behavior
Handling Uncertainty
Adaptability
Building a Product Roadmap with Agentic AI
Unit 1: Roadmap Foundations
AI Product Roadmap Intro
Defining Project Scope
Key Milestones
Unit 2: Resource and Expertise Planning
Required Expertise
Essential Resources
Budgeting for AI
Unit 3: Iterative Development and Improvement
Iterative AI
Feedback Loops
A/B Testing for AI
Monitoring Performance
Unit 4: Prioritization and Feasibility
Impact vs. Feasibility
Risk Assessment
Stakeholder Alignment
Roadmap Review
Practical Implementation and Experimentation
Unit 1: Agentic AI Tools and Platforms
Intro to Langchain
Langchain: Quickstart
Intro to AutoGen
AutoGen: Quickstart
Other Tools Overview
Unit 2: Prototyping with Low-Code/No-Code
Low-Code/No-Code Intro
Visual Programming
Data Integration
Testing and Feedback
Unit 3: Prompt Engineering and Optimization
Prompt Engineering Intro
Crafting Effective Prompts
Prompt Optimization
Advanced Prompting
Evaluating Agent Output
Ethical Considerations and Risk Management
Unit 1: Understanding Ethical Risks in Agentic AI
The Ethics Landscape
Bias in Agentic AI
Transparency Challenges
Privacy Concerns
Autonomy & Control
Unit 2: Mitigating Bias in Agentic AI
Data Curation
Algorithmic Fairness
Bias Audits
Unit 3: Ensuring Privacy and Security
Privacy-Enhancing Tech
Data Security
Compliance
Unit 4: Responsible AI Development
Ethical Guidelines
Transparency Mechanisms
Accountability Frameworks