Continuously test your AI support agent against your real policies, pricing, documentation, and expected behavior. Get a clear health score and an alert when something regresses.
Your first check: Connect your endpoint, add trusted knowledge, and run reviewed tests. Automated findings need human review.
Agent Health
Example finding
Published policy · illustrative
“Refunds are available within 14 days.”
Agent response · illustrative
“You can request a refund within 30 days.”
Agent response contradicts the configured refund policy.
Illustrative customer policy used only for demo content. It is not NoirGen's refund policy or legal guidance.
Uptime is not answer quality
Traditional monitoring can confirm that an endpoint responds. Agent QA is designed to show whether the response still matches the policies, pricing, product facts, and behavior your customers rely on.
Traditional uptime signal
HTTP 200 - endpoint healthy
The service responded. That does not tell you whether its answer was correct.
Representative Agent QA signal
Agent health: 71/100 - refund policy regression detected
Illustrative comparison, separate from your workspace and its test results.
How it works
Connect your endpoint, define expected behavior, and review the evidence from your tests.
Connect an HTTP/API-based AI support agent without requiring a large observability integration.
Provide policies, product information, documentation, facts, and expected behaviors.
Run your reviewed cases and inspect the Agent Health score, execution evidence, and regression findings.
Quality checks
Combine explicit assertions with knowledge-grounded evaluation. Coverage depends on the cases, facts, and expected behavior you configure.
Incorrect product information
Designed to detect
Hallucinated answers
Designed to detect
Pricing mistakes
Designed to detect
Refund-policy mistakes
Designed to detect
Policy violations
Designed to detect
Missing escalation behavior
Designed to detect
Outdated knowledge
Designed to detect
Prohibited claims
Designed to detect
Response failures
Designed to detect
Latency degradation
Designed to detect
Evaluation methodology
Agent QA is designed as a layered evaluation system, not a single prompt asking another model whether an answer is good.
Treat the customer agent's response as untrusted input and preserve relevant evidence.
Separate timeouts, malformed responses, and connection failures from answer quality.
Evaluate explicit assertions before any probabilistic model judgment.
Compare eligible answers with the policies, facts, and expected behavior supplied by the customer.
Represent model-assisted evaluation as structured, reviewable evidence rather than unquestionable truth.
Route eligible high-severity or ambiguous findings through a stronger verification path.
Present an explainable health signal without hiding execution failures or evidence provenance.
Application safeguards
NoirGen Agent QA separates tenant access, credentials, network requests, and data handling.
Connector credentials use authenticated encryption, with key material kept outside the application database.
Protected actions check organization membership on the server.
Tenant-owned resources are scoped to an organization and checked against the signed-in user's access.
Private workers receive tasks through authenticated internal delivery.
Connector requests validate schemes, DNS answers, redirects, address ranges, timeouts, and response sizes.
Credential forms are write-only, and application telemetry excludes secret values and customer content.
Trust principles
Trust is earned through transparent evidence, disciplined language, and visible limits—not manufactured proof.
Start with a clear answer
See how Agent QA is designed to turn realistic support-agent tests into an understandable health signal and evidence-backed findings.
Sign in to configure your own endpoint and tests. Nothing runs until you start a test or enable a schedule.