engineering · 1 min read
Loops in AI Workflows: Map, Reduce, While and For-Each
Loops are how workflows handle batches and lists. Here are the four loop patterns Bhogar AI ships and the failure modes each one prevents.
BABhogar AI TeamProduct & Engineering
Real-world automation deals in lists, not single items. The right loop primitives turn one-item demos into batch-processing systems that scale to millions.
Why it matters
Wrong choice of loop is one of the most common production bugs we triage. While loops without a hard cap run forever; for-each without parallelism is too slow; map-reduce without bounded concurrency saturates downstream APIs.
How Bhogar AI approaches it
Bhogar AI ships Map, Reduce, For-Each and While nodes with mandatory budgets: max iterations, max wall-clock and max concurrency. Bad loops are caught at design time by the linter, not at 3am.
- Map / Reduce / For-Each / While with mandatory budgets
- Bounded concurrency per loop
- Per-iteration retry and back-off
- Aggregation patterns for partial failures
- Linter warnings for missing termination
What you get
Customers using budgeted loops eliminate runaway-loop incidents and process daily batches reliably without per-batch firefighting.