CCA-F: Claude Certified Architect — Foundations

The problem described is that a single uniform output format (bullet points) is being applied to fundamentally different content types, destroying the structural characteristics that make each type useful — tables convey quantitative comparisons (revenue, margins, growth rates) far better than bullets, while prose preserves the narrative flow of news developments. The correct fix is to make the synthesis agent format-aware: render each content type in the representation best suited to it (tables for structured financial/patent data, prose for narrative news) rather than flattening everything into one generic format. Standardizing all subagents to a single output format (prose or JSON) merely shifts the mismatch problem instead of solving it, since it would force either financial data into narrative form (losing precision/comparability) or news into rigid structured fields (losing narrative flow). Inserting an intermediate common representation layer doesn't solve the underlying issue either, since the synthesis step still needs logic to decide how to display each content type appropriately — the fix belongs in the synthesis/rendering logic itself, adapting output format to content type.