Supervised vs. Unsupervised Evals for AI Agents: What's the Difference?

If you are evaluating an AI agent, the first decision you make is not which metric to track. It is whether your eval has access to a known correct answer. That single distinction separates supervised evals from unsupervised evals, and it determines where in the agent development lifecycle each one belongs.

Supervised evals compare the agent's output against an expected answer you defined ahead of time. Unsupervised evals judge behavior using only the information already present in the agent's context. Both are essential. They just do different jobs, at different stages, for different reasons.

Here is how to tell them apart and when to use each.

What Is a Supervised Eval?

A supervised eval requires knowing the correct answer in advance. That means an expected agent response, the specific tool that should have been called, or the exact documents a retrieval step should have returned.

Because supervised evals depend on ground truth, they run against a labeled dataset rather than live traffic. This makes them ideal for pre-production experiments, where you test a change against a fixed set of inputs and expected outputs to measure its impact before anything ships.

Concrete examples of supervised evals include:

• SQL semantic equivalence: Does the generated query return the same results as the expected query?
• Tool-sequence matching: Did the agent call the right tools in the right order?
• Factual correctness: Does the response match the known correct answer in the dataset?

The advantage of having ground truth is that it unlocks a wider range of precise checks. You are not asking "does this look reasonable?" You are asking "does this match what we know is correct?"

What Is an Unsupervised Eval?

An unsupervised eval assesses behavior using only the information available in the agent's own context. No expected output is required. That absence of a ground truth requirement is exactly what makes unsupervised evals suitable for continuous evaluation in production, where inputs and outputs change with every single interaction.

Concrete examples of unsupervised evals include:

• Hallucination: Did the agent state facts not explicitly supported by the context it had access to?
• Answer completeness: Did the agent address every aspect of the user's question?
• Topic adherence: Did the agent respond to questions outside the scope defined by its system prompt?
• Goal accuracy: Did the agent call the right tools to fulfill the user's intent?

None of these checks need a labeled answer. Each one can be judged from the agent's context alone, which is why unsupervised evals are what power continuous monitoring against real production traffic.

The Core Difference in One Line

Supervised evals need a known correct answer, so they run offline against a labeled dataset. Unsupervised evals need no ground truth, so they run continuously against every production interaction.

That is the whole distinction. Everything else follows from it.

When to Use Each

The two eval types map to different phases of building and operating an agent.

Use supervised evals before you ship. When you change a prompt, swap a model, or adjust a retrieval configuration, you want to know whether the change helped or hurt. Supervised evals against a labeled dataset give you that answer. Build the dataset from real production failures, and it doubles as a regression suite: re-run it before every deployment to confirm new changes do not break previously fixed behavior.

Use unsupervised evals once you are live. Most teams find out their agent is misbehaving when a user files a complaint. Unsupervised evals shift that dynamic by running against real production traffic and surfacing problems the moment they emerge, before users have to report them.

How They Complement One Another

Supervised and unsupervised evals are not competing approaches. They are two halves of a feedback loop.

Unsupervised evals running in production surface failure modes you did not anticipate. You take those failures, add them to a labeled dataset with the correct expected output, and run supervised experiments to fix them without introducing regressions. Once the fix passes, you promote it, and the same dataset becomes part of your regression suite. Meanwhile, unsupervised evals keep watching production for the next unknown.

The best practices are shared across both. Score evals as binary pass/fail rather than on a range, make each eval specific to one concrete failure mode, provide examples in the eval prompt to anchor judgment, and choose a model that balances cost and accuracy. The one thing supervised evals add is access to ground truth, which widens the range of checks you can perform.

The Takeaway

Supervised evals answer "did the agent match the known correct answer?" and belong in pre-production experiments against a labeled dataset. Unsupervised evals answer "is this behavior sound given the agent's context?" and run continuously in production because they need no ground truth.

You need both. Unsupervised evals catch problems as they happen. Supervised evals prove your fixes work. Together they close the loop that turns an unreliable agent into a production-grade one.

Want to see how supervised and unsupervised evals work together across the agent development lifecycle? Book a demo with an AI expert or explore the Agent Development Toolkit.

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