The next AI skill is not prompting.
It is knowing how to define “done.”
That is what /goal is really about.
Tools like Codex and Claude Code are starting to move beyond one-shot prompts. Not just “fix this bug,” but “fix this bug, run the test, inspect what fails, keep iterating, and stop only when the success criteria are met.”
That sounds small. It is not.
Because most AI work today has a hidden problem: the human is still the loop. Way more than they should be.
The human is still doing too much of the loop
The current AI workflow often looks like this:
The human asks. The AI answers. The human checks. The human notices the gap. The human asks again. The AI changes something. The human checks again.
At some point, everyone is just playing project manager for a robot intern with unlimited confidence.
That can be useful for small tasks, but it does not scale well for meaningful work. Real work is not just a request and a response. It is a loop: attempt, inspect, improve, verify, and decide whether the result is good enough.
/goal attempts to change that pattern.
A prompt asks for a task. A goal defines an outcome.
A prompt says: “Do this task.”
A goal says: “Do the work to reach this defined outcome.”
That difference matters because the agent now has a standard to work against. You define the objective, what success looks like, what constraints matter, what evidence proves it worked, when the agent should stop, and when it should escalate.
In coding, this is easy to see. The test passes or it does not. The build works or it does not. The bug is fixed or it is not.
But the pattern is much bigger than code.
Business work also needs a definition of done
Enterprise AI leaders should be paying attention because most business work also needs a definition of done.
Not “build a dashboard.”
But: “Create a dashboard that helps sales identify renewal risk, uses trusted metrics, and ties to a clear follow-up workflow.”
Not “write a strategy.”
But: “Create a strategy that names the business owner, expected ROI, adoption risks, governance needs, and first 30-day actions.”
Not “summarize this meeting.”
But: “Turn this meeting into decisions, owners, risks, and next steps.”
That is where AI starts becoming useful. Not when it generates more output. When it can work against a defined standard.
The real shift is from prompting to delegation
The future of AI at work is not just better prompts. It is better delegation structures.
Clear outcomes. Clear success criteria. Clear constraints. Clear evidence. Clear human checkpoints.
That is what makes an agent useful inside real work. Not autonomy for the sake of autonomy, but the ability to keep improving toward an outcome and stop with evidence.
Because vague tasks create vague output.
Clear goals create progress.
