Here’s the thing about AI agents: they’re easy to demo and hard to trust. You run through the happy path in your development environment, everything works, you ship it — and then it falls apart on edge cases you didn’t think to test, or it starts behaving inconsistently when real users ask questions in ways you didn’t anticipate. If you’ve shipped an agent and got burned this way, you’re in good company.
The fix isn’t more clever prompting. It’s evaluations — evals, in the jargon — a systematic way of measuring whether your agent is actually doing what you want it to do. Setting them up takes some upfront effort, but it’s the only way to have any real confidence in an agent you’re putting in front of people.
Why evals are different from regular testing
Traditional software tests are deterministic: you give a function an input, you know exactly what output to expect, and you assert that it matches. LLM-based agents aren’t deterministic. The same input can produce different outputs on different runs. The output needs to be evaluated on qualities like accuracy, relevance, tone, or task completion — things that aren’t easily reduced to assertEqual.
This is the core challenge evals are designed for. Rather than asserting exact outputs, you’re measuring outputs against criteria. Sometimes those criteria can be automated (did the agent include a citation? did it avoid a banned phrase?), and sometimes they need a human judge or another LLM acting as a judge.
The three types of eval you need
Correctness evals check whether the agent’s output contains the right information or takes the right action. For a customer support agent, does it correctly identify the user’s account status? For a research agent, does it find the right answer to a factual question? These often involve a reference answer to compare against, and you either use exact matching, fuzzy matching, or an LLM judge that compares the agent’s answer to the reference.
Behavioural evals check whether the agent behaves appropriately across a range of scenarios — especially edge cases and adversarial inputs. Does it refuse to answer out-of-scope questions politely? Does it handle ambiguous requests sensibly? Does it avoid hallucinating information when it doesn’t have enough context? You design these test cases deliberately, including scenarios you know are tricky.
Regression evals are about making sure things don’t break. Every time you change your prompt, update a tool, or switch model versions, you run the eval suite and check whether your pass rate has dropped. This is exactly like a test suite in traditional development — the goal is to catch regressions before they reach users.
What a basic eval setup looks like
You’ll need a dataset of test cases: inputs paired with expected outputs or evaluation criteria. Start with 20–50 examples. They should cover your happy path cases, your edge cases, and some examples drawn from real usage (once you have any).
Then pick an eval framework or build a lightweight one. Tools like Braintrust, LangSmith (part of the LangChain ecosystem), Openlayer, and Confident AI’s DeepEval all offer structured ways to define, run, and track evals. They’re not strictly necessary — a spreadsheet and some scripted assertions will get you started — but they make it much easier to track results over time and share findings with your team.
For each test case, you run the agent, collect the output, and score it. Scores can be binary (pass/fail) or graded. If you’re using LLM-as-judge for scoring — which is common for nuanced quality assessments — make sure your judge prompt is explicit about the criteria and calibrate it against a set of human-labelled examples before you trust it.
The LLM-as-judge pattern
Using another LLM to evaluate your agent’s outputs sounds circular, but it works reasonably well for certain types of assessment. The pattern looks like this:
You prompt the judge model with the task context, the agent’s output, and a rubric. The judge returns a score and a rationale. The key is to define the rubric precisely. “Was this response helpful?” is too vague. “Did the response (1) address the user’s specific question, (2) avoid including unsolicited information, and (3) provide a clear next step? Score 1–3 for each.” That’s evaluable.
The risk is that LLM judges can be biased towards longer, more confident-sounding responses, and can miss factual errors. Calibrate against human judgements regularly, especially for high-stakes use cases.
Building evals into your development workflow
The most useful thing you can do with evals is run them automatically. Set up a CI pipeline that runs your eval suite on every pull request. Gate on a minimum pass rate — if the evals drop below 85%, the PR doesn’t merge without a review. This sounds strict, but it stops you from shipping regressions without noticing.
You also want to run evals when you change models. Switching from one model version to another often produces surprising behaviour changes on specific types of input. Don’t assume a newer model is better on your specific use case — test it.
Starting from production traces
Once your agent is in production, your most valuable eval data is real usage. Log your agent’s inputs and outputs (with appropriate privacy handling), then periodically sample those logs and evaluate them. This is how you find out what your users are actually asking — often quite different from what you assumed in development — and it gives you test cases grounded in reality rather than your imagination.
Tools like LangSmith and Braintrust make it relatively straightforward to pull production traces into your eval pipeline. If you’re not using those, even a manual weekly review of a random sample of production interactions will catch a lot.
Fair enough, it takes time
Setting up a solid eval suite isn’t a weekend project. But the alternative — flying blind and finding out your agent is unreliable from user complaints — is worse. Start small: 20 test cases, a simple scoring script, and a weekly eval run. Build from there. The habit of measuring before you ship is what separates agents that stay reliable from those that quietly degrade over time.