Where AI Systems Drift, and How We Bring Them Back
AI systems don't fail in one place. They drift across layers.
Explore the stack from user intent to evaluation, inspect the controls used at each layer, and see how reliability comes from system design rather than model tuning alone.
01 Intent
User Intent Layer
Where raw requests get translated into concrete product behavior and task boundaries.
Control name
Short purpose
On-demand detail
Task decomposition
Break broad asks into smaller subtasks so the system solves the right problem in the right order.
Explanation
Prevents scope drift by turning vague intent into explicit intermediate objectives.
Pros
Reduces ambiguity early • Makes downstream prompts simpler • Improves tool sequencing
Cons
Adds orchestration complexity • Can over-fragment simple tasks
When to use
Use when a request spans multiple steps, tools, or decision points.
Failure Statistics
Teams over-attribute failure to the model
The memorable mistake is usually the model response, but the upstream causes often live in prompt design, retrieval quality, and evaluation discipline.
Where teams think AI failures happen
The visible answer gets blamed first.
Where failures actually happen
Most drift enters before or after generation.