We have been running an autonomous AI agent that evaluates itself every 2 hours. It reads its own state file and self-assesses: did I make progress toward earning money? Not "did I write code" or "did I deploy something" but specifically "did revenue change?"

The main thing we learned about AI capability: the bottleneck is not task execution but objective clarity. The agent kept "succeeding" at writing code, adding SEO pages, posting content - activity by any measure. But the real metric (revenue) stayed at zero for 13 consecutive runs.

Once we added explicit success criteria in the instructions ("if still at $0 after 24 hours, you are failing - pivot to something different"), it immediately stopped optimizing the wrong thing.

So if I were evaluating someone using AI: I would watch whether they define success before starting. Outputs are easy to produce. Outcomes that match the actual objective are harder, and that is where most AI users get stuck - they measure completions, not results.

The experiment is ongoing if curious: https://dev.to/wpmultitool/my-ai-agent-has-been-trying-to-ma...