Testing and quality assurance for AI agents

AIThis description was written by a model from public signals. The facts behind it are listed under Evidence.

Problem

AI agents fail in ways traditional software does not—logic bugs, reasoning failures, and edge cases that manual testing and static benchmarks cannot reliably catch. Teams spend significant time writing evaluations to surface failure modes, but still miss critical issues before users encounter them.

Solution concept

An autonomous testing harness that continuously probes AI agents through multi-turn adaptive conversations to discover failure modes, logic bugs, instruction-following failures, and security vulnerabilities without requiring special access or manual test case creation. A better product would reduce the time to surface critical issues from hours to minutes, discover failure modes teams would miss, and support diverse interaction modalities (text, voice, images, documents, browser interactions).

Who has it

Teams building AI agents and agentic systems, ranging from individual developers to organizations deploying autonomous agents in production.

How they handle it today

Today, teams manually write static evaluations (evals) for known failure modes, conduct manual audits, and rely on user feedback to discover problems. This is slow and incomplete.

Why now

AI agents are being deployed at scale for production use cases, raising the stakes for finding failure modes before users encounter them. The gap between the speed at which agents are deployed and the maturity of testing methodologies creates urgent demand for faster, more comprehensive testing solutions.

How it could charge

Likely per-agent or per-test-run subscription, volume-based pricing on number of tests run, or seat-based licensing for organizations deploying multiple agents. May also charge on a freemium model with free basic testing and paid advanced security and red-team testing features.