AI agent and LLM testing

We test AI agents, chatbots and LLM features the way your users will use them, and the ways people will try to break them.

Ask about AI agent testing See packages

Who it's for

Sound familiar?

You are launching a chatbot or AI assistant
Your agent calls tools or APIs and takes actions
Answers change every time you update a prompt or model
You need evidence that the AI is safe to release

What you get

Deliverables, not hours.

Example evaluation summary for an AI support assistant, showing pass rates for answer accuracy, task completion, tool use, hallucination checks, prompt injection and data leakage. — Illustrative example of an evaluation summary.

Answer quality: accuracy, relevance and tone against your knowledge base
Task completion and correct use of tools and APIs
Checks for hallucinations, inconsistent answers and off-topic behavior
Safety and security checks, including prompt injection and data leakage
Evaluation sets that catch regressions when prompts or models change
A report with failures, examples and fixes to try

How it works

How we test an AI agent

Step 1
Map
What the agent should do, what it must never do, and who uses it.
Step 2
Build the evaluation set
Real and adversarial test cases with clear pass criteria.
Step 3
Test
Exploratory sessions plus repeated automated runs of every case.
Step 4
Report
Pass rates by category, failing examples and what to change.

Questions

Good to know

Can AI be tested if answers are different every time?

Yes. We test behavior against clear criteria, run each case several times, and measure how often the agent passes, not whether one answer looked right.

Do you need access to our model?

No. We test through the same interface your users have, or through your API.