AI model reasoning is hidden, making debugging and trust impossible

Developers and power users building agentic applications or complex workflows cannot access the actual reasoning traces of AI models like Claude and GPT, only sanitized summaries or stylized UX placeholders. This makes it nearly impossible to diagnose failure modes, verify instruction compliance, or understand why a model chose a particular interpretation over another. API developers face an additional asymmetry: reasoning effort parameters (e.g., Anthropic's 'effort' levels) are configurable via API but entirely hidden from paying end-users on chat interfaces, with no indicator of what level is actually being applied. Current tools offer no toggle, no audit log, and no way to confirm the model's reasoning configuration, eroding trust especially in agentic and production contexts. The opacity also creates a secondary problem where models silently pick bad interpretations rather than asking for clarification, compounding debugging difficulty.