What This Looks Like
A small change to a prompt, field, schema, policy, example, model setting, tool contract, or workflow step produces a much larger effect later in the system. The immediate change may look minor, but downstream outputs, routes, parsers, reviews, or decisions shift more than expected.
Why It Matters
Small changes become risky when their downstream effects are not visible. A user may approve a minor edit without realizing it changes later behavior, breaks compatibility, or alters review outcomes. This makes regression testing and change control harder.
Structural Signal
A local change propagates into downstream effects beyond its apparent size or declared scope. The issue is not that change occurred; it is that the change has an amplified effect through connected workflow structure.
Common Triggers
- A small prompt edit changes output structure used by later steps
- A field label changes while parsers still expect the old label
- A policy wording change affects routing or escalation
- Examples are updated and shift model behavior broadly
- A tool schema changes in a way that breaks dependent prompts
- The workflow lacks dependency mapping for small changes
When to Use This Issue
Use this Issue when a small change creates large downstream effects across AI output, workflow behavior, tooling, review, routing, or structured data.
When Not to Use This Issue
Do not use this Issue when a large downstream effect was expected and declared. Do not use it for ordinary local edits that remain contained.