Overview
Chained Action
Multi-step action sequences executed in order
chainsequenceworkflow
Docs
Chained Actions represent multi-step workflows where each step depends on the completion of the previous one. This pattern visualizes the pipeline of operations — data fetching, analysis, generation, notification — and lets users monitor progress, inspect intermediate results, and intervene when needed. It's essential for complex AI workflows that go beyond single-prompt interactions.
Variants
- Linear pipeline — A sequential list of steps connected by arrows or lines, showing the progression from input to output.
- Branching pipeline — A workflow with conditional branches, showing different paths the execution might take based on intermediate results.
- Compact stepper — A minimal step indicator that shows progress through the chain without displaying full details of each step.
Use Cases
- Data pipelines that fetch, transform, and load data using AI
- Content workflows that draft, review, edit, and publish in sequence
- Research processes that search, analyze, synthesize, and summarize
- Automated testing pipelines that generate, execute, and report results
- Multi-model workflows that route tasks to different AI models
Best Practices
- Show the status of each step clearly: idle, active, completed, or failed.
- Display intermediate results so users can verify correctness at each stage.
- Allow users to pause, retry, or skip individual steps in the chain.
- Estimate and display expected duration for the full pipeline.
- Surface errors clearly with options to retry the failed step or restart the chain.