Test your AI agents and LLM features like the product depends on them
Grounding, hallucination, prompt injection, tool-call accuracy and safety, evaluated on every prompt, model or retrieval change. Ship AI features with evidence, not vibes.
AI features fail differently from the rest of your product. A response can be fluent and wrong, a tool can be called with the wrong arguments, and a model upgrade can change behaviour across thousands of conversations at once. Traditional assertions are not designed for any of it.
QAShift builds evaluation suites for your agents and LLM features: curated scenarios, adversarial prompts and scored checks that run whenever a prompt, model, tool or knowledge source changes.
What our ai agent testing covers
Grounding and accuracy
Answers are supported by the retrieved sources and do not invent facts.
Prompt injection
Adversarial inputs and poisoned documents cannot override instructions or leak data.
Tool use
The agent picks the right tool, passes valid arguments and handles tool errors.
Safety and policy
Refusals, sensitive topics and brand guidelines behave as intended.
Regression on model changes
Scores are compared across model versions and prompt edits before rollout.
End-to-end journeys
The AI feature is tested inside the real product flow, not just as an isolated prompt.
What it catches before your customers do
- A model upgrade makes the support bot invent refund policies
- A document in the knowledge base injects instructions into answers
- The agent calls the delete tool when the user asked to archive
- A prompt tweak improves one case and breaks forty others
Two ways to get it done
Run it yourself
Verity generates AI-feature evaluations alongside your other tests and runs them in CI, so prompt and model changes are checked like code.
We run it for you
AI agent testing is an add-on on Starter and Growth and included on Scale. Your engineer curates scenarios, reviews scores and investigates every behaviour change.
What you get
- Evaluation suites in your repo
- Scores per scenario and per release
- Prompt and trace evidence for failures
- Model-change comparison reports
Frequently asked questions
How do you test non-deterministic AI output?
With scored evaluations rather than exact matches: grounding checks, rubric scoring and property checks, run across multiple samples to measure consistency.
Which models and frameworks do you support?
Tests exercise your product’s AI features through its interface or API, so they work regardless of the model provider or agent framework you use.
Can you test retrieval-augmented generation (RAG)?
Yes. Suites check that answers are supported by the retrieved context and that missing context leads to an honest answer, not a guess.
Do you test for prompt injection?
Yes. Suites include direct and indirect injection attempts, including instructions hidden in documents and tool outputs.
Ship without fear.
Start testing free in minutes, or let a QAShift engineer deliver your first morning report within 5 days.