calibrate

Intent calibration prevents AI agents from over-interpreting ambiguous requests by reconstructing user intent before task execution. It uses transcript analysis and clean-context interpretation to verify scope, ensuring the AI only proceeds when requirements are clear.

Validate AI task scope before execution for ambiguous requests, inherited tasks, large generated outputs, and potentially irreversible operations. Applying intent calibration in these scenarios ensures planned actions match stated requirements before committing to changes.

Intent calibration applies certainty, reversibility, command, and emotional-signal checks to decide between proceeding and requesting clarification. This rule-based decision logic evaluates whether the AI's planned action aligns safely with the user's actual goal.

Yes, intent calibration works for inherited tasks and large code changes in Claude Code. It uses a clean analysis context to rebuild specifications directly from user messages, bypassing the main agent's assumptions to verify intended scope.

The best way to align AI actions with user intent is intent calibration before irreversible operations. It reconstructs specifications from user messages and applies reversibility checks to determine whether to execute safely or request clarification.

Rule-based decision logic for prompt alignment depends entirely on transcript analysis and clean-context interpretation. It may request clarification unnecessarily if user messages lack sufficient detail, though it effectively prevents overreach on ambiguous requests.