engineering · 1 min read
Sub-Workflows and Composition: DRY for AI Pipelines
Copy-pasting workflow logic does not scale. Sub-workflows turn shared steps into typed, versioned components - the right way to keep DRY in AI pipelines.
BABhogar AI TeamProduct & Engineering
The first sign your AI workflow estate is sliding into entropy is duplicated steps across canvases. Sub-workflows are the fix: extract once, version, call from anywhere.
Why it matters
Without composition, every change ripples through every copy of every workflow. Maintenance overhead grows quadratically. With sub-workflows, a fix in one place ships everywhere.
How Bhogar AI approaches it
Bhogar AI sub-workflows are first-class objects with typed inputs, typed outputs, versioning and independent deploys. Caller workflows pin to a version and can upgrade explicitly.
- Typed sub-workflow contracts
- Independent versioning and deploy
- Per-version caller usage dashboard
- Recursive call detection at lint time
- Compatible with agents and code nodes
What you get
Customers refactoring duplicated steps into sub-workflows commonly cut their workflow count 30-50% with proportional drop in maintenance cost.