Eleven AI models have tried out for me this year. Every task gets scored. The loser gets benched. This is the exact system, ready to duplicate: the log, the scoring guide, the delegation playbook, and the setup for every agent in it.

Why keep score

Everyone asks which AI agent is best. Wrong question. Reputation and results are two different games: on my board, the most famous model did the most work and won the least. A plain log of real tasks tells you more than any benchmark, launch thread, or influencer take, because it measures the only thing that matters: how the agent performs on YOUR work, in YOUR repos, briefed by YOU.

The side effect nobody warns you about: keeping score changes your own behavior. You stop arguing from loyalty. You stop re-trying a failing agent out of habit. The board decides, and the board has no feelings.

How it works

  1. Every time an agent does a real task for you, add one row to the Task Log below: the task, which agent ran it, accept or reject, a quality score out of 10, and how long it took.
  2. Weekly, open the Leaderboard view (grouped by agent): tasks done, accept rate, average quality. That is your ranking.
  3. The board picks who gets the next task. Not the brand name. Not your feelings.

Agent Task Log

The rules

  1. Score real work only, never demo tasks. Demos lie. A model that aces toy problems can still fumble your codebase.
  2. A new agent gets 10 real tasks before you judge it. Small samples produce loud wrong opinions.
  3. The leaderboard decides who gets the next task. Not habit, not hype.
  4. Anyone can get benched. Anyone can earn a comeback.
  5. Review the board weekly. The gap between reputation and results will surprise you.

Scoring guide

Keep scoring fast and consistent. One row takes 20 seconds when the anchors are fixed:

Verdict