Architectural: Copilot should detect missing source data, avoid inference, and surface uncertainty.

Users expect the AI to detect when it lacks source data, avoid inference, surface uncertainty, and adapt to environmental constraints like character normalisation. These behaviours materially improve trust and usability.

I’ve been working with Copilot on structured data extraction from a PDF and noticed a behaviour that seems like an architectural gap rather than a simple bug.

Copilot attempted to infer table structure from a template when it did not have access to the actual source data. It produced confident but incorrect output instead of signalling that the source was unavailable.  Additionally, Copilot attempted to output TAB‑delimited data, but the MS365 environment silently normalised TABs to spaces, and Copilot did not detect or adapt to this constraint.

Recommendation:

Copilot should proactively:

• detect when it lacks source data
• avoid inference when accuracy is expected
• surface uncertainty explicitly
• detect environment‑specific formatting limitations (e.g., TAB stripping)
• adapt output formats automatically

These behaviours would materially improve trust, reliability, and user experience.