Agent-as-a-Judge

Programs based on Artificial Intelligence today handle long tasks independently. Such a program, for example, writes an entire small website without a human dictating every step. But who checks whether the result is good? Asking humans is expensive and slow. That’s why a second AI program is deployed as an examiner. And when this examiner not only reads the finished text but itself searches around, tests, and double-checks, this is called Agent-as-a-Judge — roughly translated: an acting examiner instead of a mere reader.

Why comparing text is no longer enough as an evaluation

In the past, AI systems were checked almost exclusively by their final result. For a translation or a quiz question, this works well: you compare the answer with the model solution. For long, autonomous workflows, this principle breaks down. A program tasked with building a software project produces dozens of files and hundreds of intermediate steps. There is no single model solution against which this could be compared.

There is also a second problem. Two systems can deliver the same final result, but one worked cleanly while the other muddled through by chance. Anyone who looks only at the result considers both equally good. For developers, however, exactly this difference is crucial, because they want to know at which point their system goes wrong.

The practical driver is money and time. Experts who manually review hundreds of such projects easily cost several thousand euros and need days. An examining agent does the same work in minutes for a fraction of the cost. In published experiments, its agreement with human reviewers was higher than that of simple text-based evaluation.

What the examiner actually does when evaluating

The starting point is a list of requirements. Example: the data file is read in correctly, the model is trained, a chart is saved. Each requirement can be individually answered with Yes or No. The examiner works through this list point by point, instead of forming an overall judgment from gut feeling.

In doing so, it uses tools. It is allowed to search through the project folder, open individual files, actually execute program code, and read the logs of the previous work steps. It also remembers what it has already found out, so that it doesn’t read the same file ten times. In the end, it justifies each judgment with a concrete finding, such as a line number.

A related but weaker method is called LLM-as-a-Judge. There, a language model only reads the finished answer text and assigns a grade. The difference is access to the environment. And a typical misconception: the examiner is not neutral. It has the same weaknesses as the system being examined, often prefers lengthy answers, and favors systems that are similar to itself. That’s why a human spot check remains necessary as a control.

Where these examining agents are in use today

The most visible use is in programming aids. Tools that independently fix bugs in software need an automatic check of their suggestions. Similarly, this happens in benchmark tests, where competing systems solve the same tasks and an examining agent distributes the points.

In companies, the technology often operates invisibly in the background. A customer service chatbot is checked on samples after every shift: Did it give the correct information, did it observe internal rules? When training new models too, such examiners provide the feedback from which the system learns.

In news and corporate communications, terms such as evaluation, automated quality assurance, or guardrails appear for this. Anyone reading such reports should ask one question: who wrote the requirements list? Because an examining agent only checks what someone previously formulated as a requirement — and nothing beyond that.