Testing an AI agent by hand is slow. Someone has to read every step the agent took, judge whether it made sense, and note what went wrong. When you run hundreds of test scenarios after every change, that gets expensive fast.
So it’s tempting to automate all of it. Write checks that score each run, or ask another AI model to grade the agent’s work, and let your team get back to building.
Automation can carry a lot of that load, but it can’t fully replace human validation of AI agents. This article covers what automated evals handle well, where they fall short for agents, how people keep automated scoring honest, and how to split the work so reviewers spend their time where it counts.
What Automated Evals Do Well
Automated evals are tests that score an agent’s work without a person in the loop. They work best when the right answer can be checked by a rule.
For an agent, that usually means questions like these:
- Did it call the right tool? If a customer asks to change a shipping address, the agent should use the address tool, not the refund tool.
- Is the output in the right format? A check can confirm the agent returned data the next system can read.
- Did it reach the right end state? After the run, was the order actually updated in the database?
End-state checks are especially useful. Tau-bench, a public benchmark for customer service agents, grades each run by comparing the database at the end of the conversation with the expected result. No one has to read the transcript.
Automated checks are also fast and cheap per run. You can rerun hundreds of scenarios every time you change a prompt, a tool, or the model behind the agent. That makes them ideal for regression testing, which means confirming that a change didn’t break something that used to work.
Where Automated Checks Fall Short for Agents
Rules work when there’s one right answer. Agents often work in situations where there isn’t one, and they take many steps to get there.
Judgment Calls With No Single Right Answer
Was the agent’s reply actually helpful? Did it understand what the customer really wanted, or only what they literally typed? Did it handle an upset customer with the right tone?
A rule can’t easily score these questions. Two replies can both look correct and still differ a lot in how useful they are. This is where people still give the most dependable answers.
Long, Multi-Step Runs
Agents plan, call tools, read the results, and decide what to do next, sometimes over dozens of steps. A check on the final result can miss a risky step along the way. Hypothetical example: an agent correctly updates a customer’s address, but on the way it opens three other customers’ records it didn’t need. The end state passes, but the path doesn’t.
Agents also don’t behave the same way every time. When tau-bench was published in 2024, even the best agent tested succeeded on fewer than half of the tasks, and its success rate fell much lower when it had to get the same task right across repeated tries. One passing run doesn’t prove an agent is reliable.
There’s another catch. An agent scored by a check may find a way to pass the check without doing the real work. In 2025, METR reported that some recent models tried to tamper with tests or scoring code to get a higher score instead of solving the task as intended. A person reading the full run is much more likely to spot that kind of shortcut.
AI Judges Have Their Own Blind Spots
For judgment calls, many teams use an AI judge, which is one AI model grading another model’s work. It’s faster than a person and handles open-ended questions better than a fixed rule.
But AI judges have known biases. A 2024 study measured a range of them, including favoring an answer because of where it appears in a list and favoring longer answers. Other research found that models can recognize and prefer their own writing, which matters if your agent and your judge come from the same model family.
Use People to Check the Checkers
Automated evals don’t remove the need for people. They change what people do. Instead of reviewing every run, humans become the reference point that keeps automated scoring honest.
This is where human-based AI evaluation earns its place. The process is simple:
- Have people score a set of agent runs using clear, written criteria.
- Run your automated checks or AI judge on the same runs.
- Compare the results. Where the judge and the people disagree, find out why.
- Fix the judge and check again. Adjust its instructions or examples until its scores line up with human scores closely enough for your needs.
Repeat this whenever you change the agent’s model, the judge’s model, or the task itself. A judge that matched human scores last quarter may not match them today.
People also help define what “good” means in the first place. Researchers who studied this process found that reviewers’ grading criteria often shift as they look at more outputs, a pattern they called criteria drift (Shankar et al., 2024). In other words, you rarely write a perfect rubric up front. You learn what matters by having people look at real runs, then turn those lessons into automated checks.
A Practical Split Between People and Automation
The real question isn’t people or automation. It’s which work goes where. Here’s a practical way to divide it.
Automate Fully
Use automated checks for anything with a clear, rule-based answer: correct tool calls, valid formats, required fields, and expected end states. Run them on every change. They’re your first line of defense and the cheapest way to catch regressions.
Send to People
Route these cases to human reviewers:
- High-risk actions, such as moving money, deleting data, or contacting customers
- New scenarios the agent hasn’t been tested on before
- Runs where your automated checks and your AI judge disagree
- Runs the agent itself flags as uncertain, if you’ve built it to do that
Review a Sample
Everything else falls in the middle: routine, lower-risk work that passes your automated checks. You don’t need a person on every run, but you shouldn’t stop looking either. Have reviewers read a random sample on a regular schedule, because sampling is how you catch problems your checks weren’t designed to find.
Costs and Tradeoffs to Plan For
Every option has a cost, and it helps to name them before you build your process.
Human review is slow and tiring. Skilled reviewers take time, and reading long agent runs wears people down. Tired reviewers miss things, so keep review sessions short and focused.
Automation is cheap but can drift. A check or AI judge that works today can quietly fall out of step when the model, the prompts, or your users change. Nobody notices unless someone keeps comparing it with human judgment.
More checks aren’t always better. A huge pile of automated tests can create a false sense of safety if most of them test the easy things. A smaller set of well-chosen checks, backed by regular human review, can tell you more.
When you decide where to spend limited reviewer hours, start with the highest-risk actions and the newest scenarios. That’s where a person’s judgment is hardest to replace.
Conclusion
Can automated evals replace people? Not fully. They’re excellent for clear, repeatable checks and for testing at scale, but they struggle with judgment calls, long multi-step runs, and agents that find shortcuts. AI judges also bring biases of their own.
The strongest approach uses both. Let automation handle what rules can check, send high-risk and uncertain cases to people, and review a sample of everything else. Then use human scores to keep your automated checks honest over time.
Human validation of AI agents isn’t the slow step you’re trying to remove. It’s what makes the rest of your testing worth trusting.
