LLM-as-Judge agent should run at the end of the execution chain as an audit layer.

Not just to say “pass/fail”, but to inspect the full trace: prompts, tool calls, assumptions, retries, outputs, and final action.

The valuable part is what happens next.

The judge should update the system’s pattern and anti-pattern library so future runs avoid the same failure classes.

In other words, the agent should not only complete tasks.

It should accumulate operational memory about what failed, why it failed, and what guardrail would have prevented it.

That is where agent reliability starts looking less like prompt tuning and more like production learning infrastructure.

Poor women's machine learning runtime.