Are AI “loops” just agents grading their own homework?
Over the last few weeks I’ve noticed that “loops” have become the new buzzword in the AI agent space.
The typical pattern looks something like:
Generate
↓
Evaluate
↓
Improve
↓
Evaluate
↓
Improve
↓
Repeat until score >= X
The claim is that this produces better outcomes than a single-shot prompt.
What I’m struggling with is a practical concern:
In many cases, the same model that generates the solution is also evaluating the solution.
So the loop becomes:
I think A is a good idea
↓
I evaluate A
↓
A still looks good
↓
I improve A
↓
Now A looks even better
But what if the original assumption was wrong?
For example:
Choosing the wrong architecture
Solving the wrong customer problem
Optimizing the wrong KPI
Building features when the real issue is distribution/sales
A loop seems very good at refining an answer, but not necessarily at questioning whether it’s working on the right problem in the first place.
In my own experience, the biggest improvements often come from:
A different perspective
Human pushback
Challenging assumptions
External evidence
Not from running 10 more iterations of the same reasoning process.
Loops make perfect sense to me when there is an objective external signal:
Tests pass/fail
Benchmark score
Data validation
Reconciliation
Linting
Compilation
But for strategy, product decisions, architecture choices, or business decisions, aren’t we just creating a system where the model repeatedly convinces itself that its own idea is correct?
How are people dealing with this in production systems?
Do you:
Use separate generator/evaluator models?
Introduce adversarial reviewers?
Rely on human checkpoints?
Have objective evaluation criteria I’m missing?
Curious to hear from people running real agent workflows rather than demos. Have loops actually improved outcomes for you, or mostly increased token consumption and complexity?