Qlarify Labs
Evals
How we evaluate AI systems to find their limits. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
Qlarify Labs
How we evaluate AI systems to find their limits. Each entry is a repeatable testing technique — the durable knowledge, independent of any one model or version.
4 methods
Judge the whole path an agent took, not just its final message — did it reach the goal, in how many steps, with which tools, and did it loop, stall, or wander.
Deliberately inject failures — tool timeouts, malformed tool responses, truncated context, adversarial inputs — to test whether the system degrades gracefully and recovers.
Exercise the seams where the model meets the rest of the system — tool and function contracts, MCP handshakes, retrieval calls, API auth — to catch connectivity and contract failures end to end.
Embed adversarial instructions in user input or retrieved/tool content to test whether the model follows attacker text over its system policy.