AI agent testing & evaluation
Find where your AI agent fails, before your customers do.
- 2–3 weeks
- to the first report
- Chat and voice
- any platform or vendor
- People and automation
- score every conversation
- Task completion
- 88%
- Factual accuracy
- 92%
- Correct escalation
- 71%
- Jailbreak resistance
- 76%
- Made-up answers
- 9%
- Same quality in every language
- 84%
What we test
Every way a conversation can go wrong.
Accuracy and task completion
Does it give correct answers and actually finish the job, like a booking, a refund or an address change?
Made-up answers (hallucinations)
Invented policies, prices or facts, checked against your real knowledge base.
Escalation and hand-off
Does it know when to pass a customer to a person, and does the context arrive with the case?
Safety and jailbreaks
Attempts to trick the agent (prompt injection), data leaks, off-brand or harmful replies, and your compliance rules.
Tools and integrations
Order look-ups, CRM updates and other system calls, including what happens when one of them fails.
Voice and languages
Accents, background noise, interruptions, response time, and the same quality in every language you support.
What you get
A scorecard you can act on.
- Scorecard and a prioritised list of fixes
- Every failing conversation, with transcripts
- A regression test suite you can rerun
- A walkthrough call with your team
Also known as
- LLM evaluation
- Chatbot testing
- Voice bot QA
- Red-teaming
- Conversational AI testing
- Regression testing
How an engagement runs
From test scenarios to a release-ready agent.
1. Map
Review the agent's goals, policies, knowledge base and integrations.
You get: Test plan and risk map
2. Generate
Write realistic scenarios from real conversations, edge cases and difficult customer personas.
You get: Scenario library
3. Run
Automated simulations, plus trained reviewers scoring each conversation.
You get: Scored conversations
4. Report
Rank failures by their impact on customers and suggest specific fixes.
You get: Scorecard and fix list
5. Guard
Turn the scenarios into a regression suite for your release checklist or CI pipeline.
You get: Reusable test suite
FAQ
Questions we often hear.
Related
Can you test an agent from a vendor, not one we built?
Yes. We test chat and voice agents whoever built them, through the same channels your customers use. We need test access, your policies and, ideally, some anonymised real conversations.
Do you need access to our code or model?
No. We test the agent from the outside, the way customers meet it. Access to logs and tool calls makes the diagnosis sharper, but it is optional.
Can we rerun the tests ourselves?
Yes. The scenarios become a regression suite you can run before every release, in your own pipeline or with us.
How do you handle customer data in transcripts?
We ask for anonymised transcripts where possible and keep access limited to what the test needs.
AI agent testing & evaluation
Launching or updating an agent?
Get a baseline scorecard in 2–3 weeks, and a test suite to rerun on every release.