A vague requirement: 'The system should ingest customer logs and provide a real-time...

A vague requirement: "The system should ingest customer logs and provide a real-time summary dashboard."

To a data engineer, that lacks the definitive context required to protect data privacy, prevent model drift, or guarantee compliance.

To bridge this execution gap, I advocate IBM'S 5 Layer Context Model.

An AI Epic cannot be committed unless it satisfies all 5 Context Layers.

Layer 1 is Intent: you must explicitly anchor model objectives directly to real-world user operations and localized environment context.

Layer 2 is Data: you restrict data inputs strictly to governed datasets.

Layer 3 is Explainability: you programmatically force the system to translate its internal logical path into clear human interfaces.

Layer 4 is Feedback: you design UI for frontline operators to instantly flag, adjust, or correct non-deterministic inaccuracies.

Layer 5 is Escalation: you map out operational handover procedures the second confidence thresholds drop below your target bounds.

By enforcing these five layers at the epic template level, you operationalize trust directly inside your daily project management metrics.

You stop guessing what an automated agent will do in production because you have engineered its operational perimeter before writing a line of code.