friction-report

To document developer experience friction from AI-assisted coding sessions, analyze the conversation history for build failures, misleading errors, and documentation gaps, then generate a structured friction report with severity levels and actionable improvements.

Session friction analysis in AI agent development is the process of scanning conversation history to identify and document developer experience obstacles such as SDK surprises, build issues, and agent workflow problems that occurred during a coding session.

You generate a structured friction report by reviewing completed AI-assisted coding sessions, extracting expected behavior and actual outcomes for each friction point, assigning severity levels, and categorizing sanitized findings into actionable improvement areas.

You need to run a friction report at the end of an AI-assisted coding session to capture recurring developer pain points, prevent lessons from workflow issues or unclear documentation from being lost, and prepare a structured review for human analysis.

Yes, you can use a friction report to analyze misleading errors and SDK surprises by reviewing the session conversation history, identifying where actual outcomes diverged from expected behavior, and documenting these findings with resolutions and severity levels.

Automatically capturing AI development friction is limited to analyzing conversation history from completed sessions, meaning it cannot identify real-time issues during active coding and relies on the conversation containing sufficient context about the build failures and workflow problems.