Scorecard Framework Aims to Measure If Work Items Are Documented Enough for AI Agents

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A developer has proposed a 100-point scorecard to assess whether work items contain sufficient documented evidence for AI agents to act on and for humans to review. The rubric spans five dimensions — problem, reproduction, expected behavior, verification, and limitations — each worth 20 points and rated as fully documented, partial, or missing. The framework was tested against a specific MonkeyCode issue and pull request, yielding a score of 80 out of 100, with reproduction and limitations flagged as gaps. Rather than blocking work outright, the scorecard is designed to adjust what an agent is permitted to do based on coverage level, such as allowing investigation but preventing automatic merges. The author recommends treating it as a product experiment, tracking metrics like clarification rounds and reopened issues to gauge its real-world value.

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