Stress-Test Your AI Agent’s Defenses Against Hallucination
Is your AI agent production-ready?
Hallucination is the number one reason enterprise agents get pulled back out of production. This assessment measures your agent’s defenses against the reliability bar your use case actually requires, then shows you exactly where it would break first.
What does your agent actually do?
Production-ready is not an absolute. The hallucination rate you can live with depends entirely on what is at stake when the agent gets it wrong. Pick the closest match. It sets the bar everything else is judged against.
One question. What would yours do?
Your defense stack, layer by layer
Toggle a thin layer to assume hardened and watch your readiness move. This is what closing the gap buys you.
Your top exposures
The cost of a confident wrong answer
The contract value of the report Deloitte Australia partially refunded to the Australian government in October 2025, after fabricated citations and an invented court quote were found in it.
Once hallucination rates pass roughly this mark in high-visibility deployments, users abandon the agent even when later answers improve. A few wrong answers undo more trust than a hundred right ones build.
The share of legal queries on which general-purpose language models hallucinated in Stanford’s systematic study. In regulated work, “usually right” is not a strategy.
This scorecard is an indicative self-assessment based on the controls you reported, not a measurement of your agent’s live output. Residual-rate ranges are directional and drawn from published benchmarks for comparable control stacks; your real rate depends on your data, models, and traffic. Use it to find where to look first, then measure for real. Figures cited: Dahl et al., “Large Legal Fictions,” Journal of Legal Analysis (2024); AP and Guardian reporting on the Deloitte Australia / DEWR report (October 2025); Atlan analysis of enterprise agent deployments (2026).