engineering · 1 min read
Designing Conditional Branches in AI Workflows
Conditional branches are where AI workflows turn from linear scripts into real automation. Here are the four patterns we use most often.
BABhogar AI TeamProduct & Engineering
A workflow without conditionals is a script. Conditionals are where you encode actual decision logic and stop pretending every input deserves the same treatment.
Why it matters
Common mistakes: too many nested ifs, conditionals on free-text without classification, and hidden coupling between branches. The fix is structured classification plus single-purpose branches.
How Bhogar AI approaches it
Bhogar AI ships a Decision node that runs a small classifier (rule-based or LLM-based) and routes to typed branches. Branch definitions are explicit, lintable and observable per branch in the run dashboard.
- Decision node with rule + LLM classification modes
- Typed branches with auto-generated schemas
- Per-branch latency and cost dashboards
- Lint warnings on dead or overlapping branches
- A/B harness to test branch logic
What you get
Refactoring deeply-nested ifs into Decision nodes cuts mean workflow latency 15-25% and reduces incident frequency.