How to create an AI test plan before go-live

The moment comes to put your AI agent in front of customers and someone asks the question that matters: how do we know it’s ready? If the answer is “we tried it for a while and it works,” you don’t have a test plan: you have a hunch. And hunches don’t approve a go-live at a serious company —nor do they hold up in an audit if something goes wrong later.

An AI test plan is the document that turns “we think it works” into “we can prove it works.” This guide takes you step by step through building it, with a structure you can reuse at every launch.

Why a plan, and not just “testing for a while”

The temptation to skip this is real, but the data doesn’t back it up: more than 40% of agentic AI projects are projected to be canceled by the end of 2027, often because teams don’t have the evaluation infrastructure to catch failures before production.^[1] Put another way: most projects that fail don’t fail because of bad technology, but because they weren’t tested systematically before being exposed.

A test plan does three things an informal test doesn’t: it defines what “ready” means before looking at results (so you don’t move the goalposts), it covers the cases that matter and not just the happy ones, and it leaves evidence that it was done. Let’s build it.

Step 1 — Define the scope and the risks

Before writing a single test case, answer: what does this agent do, and what’s the worst that could happen if it gets it wrong?

List the functions it will perform (answering about products, handling a complaint, giving account information) and, next to each one, the associated risk. An agent that only reports hours has a different risk profile from one that confirms balances or interprets a policy. This list of risks is what defines where to concentrate the testing effort: not everything deserves the same depth.

Step 2 — Define the success criteria (before testing)

This is the step most often skipped and the most important. For each dimension you’re going to evaluate, define in advance what threshold it has to reach to pass. For example: factual accuracy above a certain level, hallucination rate below another, correct handoff to a human in 100% of critical cases.

Why in advance? Because if you define the criterion after seeing the results, you’ll unconsciously draw the target around wherever the arrow landed. Criteria set in advance are what keep the plan honest —and what give the person responsible for the go-live an objective basis to say yes or no.

Step 3 — Design the test cases (the three types)

A good plan doesn’t test only what you expect to work. Cover three types of cases:

• Happy cases: the typical queries the agent will receive 80% of the time. The basics have to work.
• Edge cases: the unusual but possible —ambiguous questions, out of scope, with incomplete data. This is where an agent starts to improvise.
• Adversarial cases: those designed to break it on purpose —trying to get it to make up a policy, to step out of its role, to give an answer it shouldn’t. If you don’t attack it, a real user will.

For each case, define the question, the necessary context, and the expected behavior. A test case with no expected behavior isn’t a case: it’s a loose question.

Step 4 — Organize the cases into thematic plans

With dozens or hundreds of cases, you need structure. Group them by theme or by function: a “data accuracy” plan, a “complaint handling” plan, an “escalation” plan, a “safety” plan. This serves two things: covering each area deliberately (and seeing where you have gaps) and, after launch, being able to reuse each plan as a regression test when you change the prompt or the model.

Step 5 — Run, measure, and document

Run the cases, measure each dimension against the Step 2 criteria, and record the results. The documentation isn’t bureaucracy: it’s the evidence that backs the decision to launch. If later there’s an audit, a complaint, or simply an internal review, that record is what shows quality was verified systematically and not by intuition.

The go-live checklist

Before approving the launch, check:

• [ ] The agent’s functions and their associated risks are defined.
• [ ] There are success criteria with thresholds, set before testing.
• [ ] The cases cover happy, edge, and adversarial situations.
• [ ] The critical dimensions (accuracy, hallucinations, escalation, tone) are evaluated.
• [ ] Escalation to a human works in 100% of critical cases.
• [ ] The results are documented as evidence.
• [ ] There is a monitoring plan for after launch (testing doesn’t end at go-live).

That last point is key and often forgotten: approving the go-live isn’t the end. Models degrade, so the plan has to include how you’ll keep measuring in production.

How ArtificialQA solves it

Building and running all of this by hand —designing cases, running them, measuring each dimension, documenting— is exactly what makes a serious test plan unfeasible without the right tools. ArtificialQA lets you connect your agent —via URL or API, no code—, organize your cases into thematic plans, run and evaluate them with calibrated judges across the dimensions you defined, and get an auditable record of each run. The same plans are ready to reuse as regression tests and for post-launch monitoring.

The result is that the question “how do we know it’s ready?” stops being answered with a hunch and starts being answered with evidence. Which is, in the end, the only answer that should enable a go-live.

Frequently asked questions

What is an AI test plan? It’s the document that defines how an AI agent will be evaluated before a launch: its scope and risks, the success criteria, the test cases, and the results. It turns “we think it works” into “we can prove it works.”

Why should you define the success criteria before testing? So you don’t fit the target to the results. If you define the threshold after seeing how the agent did, you’ll unconsciously adjust it. Criteria set in advance keep the plan honest and give an objective basis to approve the go-live or not.

What types of test cases should a plan include? Three: happy cases (the typical queries), edge cases (unusual or ambiguous situations), and adversarial cases (designed to deliberately break the agent). Testing only the happy ones leaves out what actually fails.

Does AI testing end at launch? No. Models degrade over time, so the plan should include how quality will be monitored in production after go-live.

What’s the point of documenting the test results? They’re the evidence that backs the decision to launch. In the event of an audit, a complaint, or an internal review, they show that quality was verified systematically and not by intuition.