AI Loops Require Goals. Do AI Hyper-Loops Require Hyper-Goals?
This is a rough architecture idea that just popped up in my head after reading about AI Loops and Goals. I’m trying to name a control problem I think might become more important as agentic systems scale beyond loops.
| Level | Name | Description |
|---|---|---|
| 1 | Prompt | A one-shot instruction. The model answers, then the process ends. |
| 2 | Agent | The model can use tools, call APIs, search, write files, or execute code. |
| 3 | Loop | The agent iterates toward a defined goal: think, act, observe, adjust. |
| 4 | Hyper-Loop | Many loops run in parallel or coordination, such as design, verification, critique, search, simulation, and risk analysis cross feeding each other. |
The problem is that a loop needs a goal, but a simple goal, as we all know, can become stupid if it turns into blind metric optimization.
Example:
>Make the code faster.
An AI Loop may improve speed by removing error handling, reducing readability, or breaking edge cases.
This is basically Goodhart’s Law:
>When a measure becomes a target, it ceases to be a good measure.
So my thought is:
>AI loops require Goals. AI Hyper-Loops require Hyper-Goals.
But what is a Hyper-Goal? Is it a supervisory goal that checks whether lower-level goals are still serving the real objective and how do you formulate a real objective?
Some examples:
| Normal goal | Possible Hyper-Goal ??? |
|---|---|
| Make the code faster | Improve speed without reducing correctness, maintainability, readability, test coverage, or security. |
| Reduce system weight | Reduce weight only if safety, reliability, manufacturability, serviceability, and compliance remain acceptable. |
| Pass an audit | Improve real process maturity and evidence quality, not just documentation appearance. |
| Complete a functional safety case | Do not increase confidence unless evidence quality has increased. |
A few shorter Hyper-Goal examples:
Do not optimize the metric if doing so damages the reason the metric exists.
Do not improve one KPI by moving risk into an unmeasured part of the system.
So the distinction would be:
| Level | Needs |
|---|---|
| Prompt | Clear instruction |
| Agent | Tools and task context |
| Loop | Goal |
| Hyper-Loop | Hyper-Goal |
Does this make sense, or is this already covered by existing terminology/research in agent architecture?
How would you implement this? I honestly have no clue but it was fun thinking about it.....
PS: If you wonder why the examples are a bit odd it is because those are my areas of interest and where I use AI a lot.