AI Workflow Design Patterns: 7 Patterns Every Builder Should Know

Pattern 1: Sequential Chain

Step A → B → C, where each step's output is the next step's input. Use when: task has clear ordered stages.

Pattern 2: Parallel Fan-Out

Split one input into multiple parallel tasks, merge results. Use when: independent subtasks can run concurrently for speed.

Pattern 3: Router/Classifier

First step classifies the input, routes to specialised handler. Use when: different input types need different treatment.

Pattern 4: Fallback Chain

Try primary approach. On failure or low confidence, try secondary. Use when: reliability is critical.

Pattern 5: Map-Reduce

Apply same operation to many items (map), aggregate results (reduce). Use when: processing collections — summarise 50 reviews, then synthesise themes.

Pattern 6: Human-in-the-Loop Gate

AI generates output, pauses for human approval, resumes based on decision. Use when: outputs affect real systems.

Pattern 7: Iterative Refinement

Generate → critique → revise → repeat until quality threshold met. Use when: quality matters more than speed.

Apply These Patterns to Your Business

Our workshop is structured around these 7 patterns — you'll implement at least 4 with real business use cases.