AI Evaluation Tools: How to Choose
What AI evaluation tools do, the categories that exist, and how to pick one for evaluating agents — not just model outputs.
AI evaluation tools measure the quality of AI outputs — scoring correctness, groundedness, safety and more using metrics, LLM-as-a-judge graders and human review. They fall into three camps: open-source eval frameworks (DeepEval, Ragas, OpenAI Evals), observability platforms that added evals (Langfuse, LangSmith), and eval-first commercial platforms. For agents specifically, the deciding question is whether the tool evaluates the whole agent in production or just model outputs at development time.
What do AI evaluation tools do?
They turn 'does this output look right?' into a measurable score. A typical tool lets you define a dataset of test cases, apply graders (rule-based metrics, LLM-as-a-judge against a rubric, or human ratings), and track the results over time on dashboards. The good ones run both offline — gating changes in CI against a fixed dataset — and online, sampling live traffic so quality is monitored continuously rather than measured once.
What are the main categories of AI evaluation tools?
Three, solving different problems. Open-source eval frameworks (DeepEval, Ragas, OpenAI Evals) give you metrics and a test harness as code — strong for offline, developer-run evaluation, and free to adopt; you build the production side yourself. Observability platforms with evals (Langfuse, LangSmith) attach scores to the traces they already capture — strong on scoring what happened, lighter on completion verdicts and per-agent quality trends. Eval-first commercial platforms centre the workflow on datasets, experiments and judge calibration — strongest for large structured eval programmes before deploy.
AI evaluation tools vs agent evaluation tools — what's the difference?
Most AI evaluation tools were built to score a model's input and output — one prompt, one completion. Agents are more than a model: they call tools, retrieve context, and act over multiple steps. Evaluating an agent means scoring whole sessions — task completion, tool-use correctness, groundedness, policy adherence — on live production traffic, not just single completions in a notebook. When you are shipping agents, look for a tool that understands sessions, sub-agents and tool calls, and that runs in production, not only at dev time.
How do you choose an AI evaluation tool?
Decide by where your gap is. If you have no offline testing, start with an open-source framework this week — it is the cheapest way to stop regressions. If you can score outputs but have no visibility into live quality, you need online sampling and per-agent trends. Check three things: does it evaluate the whole agent or just the model; does it run in production or only in CI; and does it fit the traces and stack you already use, rather than forcing a switch. Prefactor sits at the agent quality layer — evals plus human feedback plus per-agent cost and quality analytics, in production.
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