{"id":19755,"date":"2026-04-21T13:43:33","date_gmt":"2026-04-21T13:43:33","guid":{"rendered":"https:\/\/www.comet.com\/site\/?p=19755"},"modified":"2026-04-21T14:26:11","modified_gmt":"2026-04-21T14:26:11","slug":"ai-agent-regression-testing","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/ai-agent-regression-testing\/","title":{"rendered":"Introducing Opik Test Suites: Straightforward Unit & Regression Testing for AI Agents"},"content":{"rendered":"\n

One of the biggest challenges when it comes to agent development is quality. It\u2019s getting easier every day to spin up an MVP or demo of an agent that accomplishes complex tasks through an array of tool calls, context retrieval steps, and system prompts. But it\u2019s still hard to know whether that agent will perform consistently, predictably, and safely in production. With LLM calls in the mix, small situational differences cause different outcomes, and it\u2019s hard to broadly and repeatably define what good looks like. The core of the problem might not actually be quality, but rather the ability to measure quality, or even define what it looks like.<\/p>\n\n\n\n

When these challenges come up, the most commonly cited solution is \u201cevaluation.\u201d The advice is to use AI evaluation techniques, to score how well an agent performs on various metrics. Typically evaluation processes involve building a large dataset of examples (possible inputs to the agent) and defining customized metrics to score them against. The outcome is a numerical score, indicating how well the agent does on the metric in question (e.g. a score of 0.9 on a \u2018correctness\u2019 metric). Until now, nearly all AI evaluation platforms \u2014 Opik included \u2014 have taken this approach.<\/p>\n\n\n\n

AI Evaluation vs. Software Testing: Simplifying the Workflow<\/h2>\n\n\n\n

Evaluation as described above works pretty well if you have time to surface, evaluate, and debug individual issues \u2014 but it doesn\u2019t work well for AI builders who want to move with the speed and efficiency today\u2019s development cycles require. Building evaluation datasets from scratch takes significant time and effort. Even defining metrics is easier said than done. The most effective metrics are customized to the specific use case, often taking the form of LLM-as-a-judge<\/a> metrics, where another LLM is prompted to evaluate the agent\u2019s performance given precise criteria. LLM-as-a-judge metrics require lengthy prompts covering how to score a wide range of scenarios the agent might encounter.<\/p>\n\n\n\n

The scores they produce give a general indication of the agent\u2019s adherence to the metric, but they don\u2019t help much when it comes to actually improving the agent. It\u2019s not very clear what to do about a \u201cusefulness\u2019 score of 0.6. Given all the friction in the process and the unhelpful results, it\u2019s no surprise that many AI developers skip systematic evaluation entirely.<\/p>\n\n\n\n

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As the Opik team saw users go through the evaluation struggle again and again, the question eventually became, \u201cWhat if evaluation (as we are doing it now) isn\u2019t really what we need?\u201d<\/p>\n<\/blockquote>\n\n\n\n

The dataset-based approach to evaluation came from the methods used to characterize individual models, for example, scoring how much a particular LLM is prone to hallucination.<\/p>\n\n\n\n

Developing agents, however, is very different from developing models. In fact, it\u2019s a lot more like building any other kind of software. By the same logic, we ought to be testing agents the same way we test other software.<\/strong><\/p>\n\n\n\n

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AI builders don\u2019t need a general sense of how often their agent hallucinates, but rather a structured test that identifies clear failure modes, informs fixes, and concretely indicates whether the agent is ready to go live or not.<\/p>\n<\/blockquote>\n\n\n\n

For any type of software (including agents), engineers build test suites of concrete scenarios (\u2019test cases\u2019) and define rules for how the software should behave in each case. Running the test suite produces a list of passed and failed tests. Each failure is tied to a specific rule (assertion) that was broken, already pointing towards a fix. Every time the code changes, they run regression tests to make sure software still runs as expected and passes every test.<\/p>\n\n\n\n

This works well for standard software, but a few adjustments are needed to make it work for agents. Standard software has constraints on what the possible inputs and outputs of a given function can be. It\u2019s deterministic \u2014 the same input typically results in the same output, making it easy to define rules. Agents can give many different equally correct responses to the same input, meaning that strict rules about what is \u2018correct\u2019 are not possible.<\/p>\n\n\n\n

Introducing Opik Test Suites<\/h2>\n\n\n\n

To address this challenge, we built Test Suites in Opik, as a way to test agents with same level of rigor we test software. Opik\u2019s Test Suites use the structure and logic of software testing, and incorporate LLM-as-a-judge techniques under the hood to handle the unpredictable nature of agents.<\/p>\n\n\n\n

Here’s how we think about Test Suites. Testing an agent should work the way regression testing works for any other software: clear pass requirements, actionable results, and a suite that’s easy to re-run after every change. Those pass requirements need to be concrete, without arbitrary scores that have to be interpreted to figure out what counts as “good enough.”<\/p>\n\n\n\n

\"ai<\/figure>\n\n\n\n

When something fails, the result should point straight to a specific failure mode. You should know exactly which scenario broke and which rule it broke, so the path to a fix is obvious. That’s why Test Suites support both global assertions that apply across the entire suite and item-level assertions tailored to individual test cases, with failure modes that are concrete in the same way they are in software testing.<\/p>\n\n\n\n

Writing those rules should also be easy. AI builders need not spend half of their time figuring out which metrics to use or crafting lengthy evaluation prompts to handle every possible scenario. Opik does the heavy lifting to transform simple rules into LLM-as-a-judge prompts according to best practices.<\/p>\n\n\n\n

And because the whole point is catching real failures, your suite should grow as you build. Instead of constructing a dataset up front, you add traces from the problems you find as you test and debug, so your test coverage compounds alongside your agent.<\/p>\n\n\n\n

Getting Started with Test Suites<\/h2>\n\n\n\n

Test Suites are designed to be both easy and accurate, with Opik handling complex evaluation workflows for you. All you have to do is log some agent activity using Opik\u2019s tracing feature (docs here<\/a>), define an assertion in plain English, and Opik will test your traces against it, providing clear pass\/fail results. Here\u2019s Opik Head of Product Jacques Verr\u00e9 with a walkthrough:<\/p>\n\n\n\n

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