AI Systems Thinking: How to Design Workflows That Scale

Applying systems thinking principles to AI workflow design for scalable, maintainable solutions. Covers systems theory for AI, feedback loop design, emergent behavior in AI pipelines, bottleneck analysis, resilience patterns, multi-agent coordination, workflow orchestration architecture, observability and debugging, and designing AI systems that improve themselves over time.

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Dernière mise à jour	22/03/2026
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Cours
Avis

Introduction to Systems Thinking in AI

10Leçons ·
- What is Systems Thinking?
- What is Systems Thinking? - Quiz
  
  10 xp
- Components of AI Systems
- Components of AI Systems - Quiz
  
  10 xp
- Designing Workflows
- Designing Workflows - Quiz
  
  10 xp
- Feedback Loops in AI
- Feedback Loops in AI - Quiz
  
  10 xp
- Implementing Systems Thinking
- Implementing Systems Thinking - Quiz
  
  10 xp
Feedback Loop Design

8Leçons ·
- Types of Feedback Loops
- Types of Feedback Loops - Quiz
  
  10 xp
- Designing Feedback Mechanisms
- Designing Feedback Mechanisms - Quiz
  
  10 xp
- Evaluating Feedback Effectiveness
- Evaluating Feedback Effectiveness - Quiz
  
  10 xp
- Iterative Improvement Using Feedback
- Project: Implementing Feedback in Workflows
Emergent Behavior in AI Systems

5Leçons ·
- What is Emergent Behavior?
- Case Studies of Emergent Behavior
- Designing for Emergent Behavior
- Strategies for Managing Emergence
- Project: Designing for Emergence
Resilience Patterns in AI Systems

5Leçons ·
- Understanding Resilience in AI
- Resilience Patterns Overview
- Designing Resilient Workflows
- Testing for Resilience
- Project: Implementing Resilience Patterns
Workflow Orchestration and Observability

6Leçons ·
- Principles of Workflow Orchestration
- Principles of Workflow Orchestration - Quiz
  
  10 xp
- Designing Observability into Workflows
- Monitoring and Debugging Strategies
- Project: Orchestrating AI Workflows
- Final Project: Building a Self-Improving AI Workflow