Evals · AI agent evaluation
Eval tool or eval service: which does your AI agent need?
An eval tool gives you the machinery: tracing, datasets and LLM-as-judge runners. An eval service gives you the people and process to decide what good means, build the golden dataset and judge, and act on the results.
Tool and service side by side
These are two different things. Neither one replaces the other.
| Topic | Eval tool | Eval service |
|---|---|---|
| What it does | Captures traces, stores datasets, runs scorers and LLM-as-judge prompts, and shows results. | Looks at your production traces, runs error analysis into a failure taxonomy, builds the rubric, golden dataset and judge, and turns findings into changes. |
| Who does the work | Your engineers set it up, write the evals and read the dashboards. | A team of evals consultants works with your engineers and domain experts, or does the work for them. |
| What you own at the end | A running system and whatever evals your team wrote. | A golden dataset, a rubric, a judge checked against expert labels, and regression tests in CI, kept in your tool and your code. |
| When it fits | You know what good looks like and need the machinery to measure it at scale. | You cannot yet say what good looks like, your experts disagree, or nobody has time to read traces and act on them. |
What a tool does not decide for you
A tool records what your agent did. It cannot tell you which failures matter to your users or what a correct answer looks like in your domain. People who know the domain have to decide that. Your own team can own this work, or an eval service can do it with you.
Tool, no one owns the process
- ×Traces are logged, but no one is assigned to read them.
- ×The judge prompt is not checked against expert labels.
- ×Scores change, but no one has defined what better means.
Tool, with an owned eval process
- ✓Error analysis turns traces into a list of failure modes.
- ✓Domain experts write the rubric and label a golden dataset.
- ✓The LLM-as-judge is checked against expert labels, then runs as regression tests in CI.
What teams report
The LangChain State of Agent Engineering report surveyed over 1,300 professionals. It reports a gap between watching agents and evaluating them.
Example eval tools
Each description below comes from the tool's own page. Follow the link to read it in full.
- Braintrust describes itself as an observability platform for agents that turns production patterns into evals.
- LangSmith documents observability that gives visibility into LLM applications, from single traces to production performance.
- Arize Phoenix describes itself as AI observability and evaluation, with a workflow for debugging and iteration.
- Langfuse describes itself as an open-source AI engineering platform for debugging, analyzing and iterating on agent applications.
- Promptfoo describes itself as an open-source CLI and library for evaluating and red-teaming LLM apps.
- DeepEval is a framework for evaluating LLM apps, with an optional hosted platform.
An example of an eval service
Clarity by Epistemic Me offers evals consulting as part of its AI-native services. It runs done-with-you or done-for-you and starts at $100k+. Clarity is an official partner of Parlance Labs. The client keeps the dataset, rubric, judge and code. See the Dayforce case study.
Not sure which you need?
Tell us about your agent. We will say whether a tool, a service or both fits.