analiza aiqbench
This guide outlines rigorous evaluation methods for AI agents, specifically focusing on distinguishing between failures in tool selection versus incorrect argument passing. By employing deterministic checks, LLM judges, and trajectory comparisons, developers can systematically validate the reliability of complex agent workflows before deployment.
How to Test Tool-Calling Accuracy in AI Agents
An agent can call the wrong tool, or call the right tool with the wrong arguments. This guide covers three ways to test each failure mode, a Python harness that grades both, and how to run the same test cases against several tool-capable models through OpenRouter.
An agent can fail in two places when it uses a tool. It can choose the wrong tool, or it can choose the right one and send the wrong arguments. Those failures tell you different things. If an agent calls lookup_order instead of refund_order, the problem is tool selection. If it calls refund_order with the wrong order_id, it chose the right tool and passed the wrong arguments. This guide covers three ways to test tool-calling behavior. The first is a reference-free large language model (LLM) judge. The second is deterministic argument checks. The third is trajectory comparison. It then shows how to run the same test cases against several tool-capable models through OpenRouter. Test tool selection and argument correctness separately. An agent can choose the wrong…
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