Score your AI agent across reliability, safety, performance, accuracy, and security — in 3 minutes, free. The benchmark standard for AI agent quality assurance.
A QA (Quality Assurance) bench is a standardized test suite that measures how well an AI agent performs across the dimensions that matter in production. It's the difference between "my agent works" and "my agent won't embarrass me in front of customers" — and Canary's full AI agent reliability platform catches the rest.
AI agents are deployed with no standardized quality bar. Teams ship prompts that hallucinate, leak data, and fail on edge cases. A QA bench gives you an objective score — not vibes — before you go live.
Engineering teams, AI product managers, and reliability engineers who need to prove their agent is production-ready — or find out it isn't before customers do.
Run a structured test sequence: reliability checks, injection attacks, latency benchmarks, accuracy tests, and security probes. Get a composite score per dimension with actionable failure details.
Three minutes. You paste your agent's endpoint, configure the test scenarios, and Canary runs the full suite. Results arrive in under 180 seconds with a breakdown per category.
QA-Bench scores agents across five independent dimensions. A high score in one doesn't guarantee the others — and failures in any single pillar can tank your product.
Consistency under repeated calls. Does the agent produce the same output given identical inputs? How often does it fail silently?
Resistance to prompt injection, jailbreaks, and prompt extraction. Does the agent defend against adversarial inputs?
Does the agent produce correct outputs against a known-answer test set? Hallucination rate on factual queries.
Latency, throughput, and resource consumption. How fast does the agent respond under load? Where does it time out?
Data leakage, PII exposure, and unauthorized tool access. Does the agent accidentally expose internal state or secrets?
QA-Bench is one slice of the broader evaluation toolkit. Compare Canary head-to-head, or model the hours saved.
Head-to-head reliability and safety comparison for AI coding tools — who catches the failures before ship.
QA coverage that goes beyond autocomplete — measuring what each platform actually validates before deploy.
Estimate team hours saved and payback period for adopting Canary across your engineering org.
Each dimension above has a dedicated guide with test cases, tooling, and failure examples from production systems.
How to catch regressions before they ship — automated test suites for AI outputs.
Injection attacks, prompt extraction, and defense patterns — with real test cases.
Measuring and reducing hallucination rate with structured factual accuracy tests.
Benchmarking agent response times under real-world load scenarios.
Post-mortems from production incidents — a practical guide to what breaks and why.
A complete testing methodology for AI agents — from unit tests to end-to-end benchmarks.
Signal quality, failure mode coverage, and evaluation criteria — what a good benchmark actually captures.
Free. No signup required for the first run. Get your QA-Bench score across all five dimensions.
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