Ronaldo Galdino
Problem:
The current AI Usage dashboard provides a high-level overview but lacks the necessary granularity for effective resource management. Currently, while Super Agents have a clear breakdown, other high-consumption features like Autopilot Agents, AI Cards, AI Fields, and AI Tasks only show aggregate costs. Furthermore, the dashboard prioritizes monetary value (USD) over the actual credit count, and lacks historical month-over-month reporting for trial/bonus pools.
Proposed Solution:
I suggest a comprehensive update to the AI Usage interface to include:
  • Dual Metric Visibility: Display "Credits Consumed" alongside "Monetary Cost (USD)" as standard columns. Relying on tooltips (hovering) makes it difficult to compare usage across different workflows quickly.
Granular Breakdown for All AI Features: Extend the "Super Agent" level of detail to Autopilot Agents, AI Fields, and AI Cards. We need to see:
  • Which specific Agent/Automation triggered the usage.
  • The location (List/Folder/Space) of the trigger.
  • The User/Member responsible for the action.
  • Historical Data & Period Filtering: Ability to filter usage by previous months or custom date ranges. Currently, the breakdown resets on the 1st of each month, making it impossible to audit past consumption patterns once the month ends.
  • Audit Log Integration: A dedicated "AI Audit Log" or better filtering in the existing Automation Audit Log to specifically isolate AI-related consumption events.
Why this is important:
As a decision-maker, I cannot confidently purchase additional AI Credit packs or add-ons without understanding exactly how our current credits are being spent. Clear visibility is the bridge between a "trial" and a "paid" commitment. Better governance tools will help teams optimize their workflows and scale their AI usage responsibly.