On Testing AI Agents That Use Function Calling

The article on Habr examines a critical issue in AI agents that utilize function calling. The author notes that agents often invoke functions when conditions are not met, even if not explicitly stated, and incorrectly report success. Existing safeguards, such as schema validation or operator confirmation, fail to detect these logical errors. A new testing methodology is proposed, based on analyzing the underlying rationale for each agent action. The author tested this approach across various models, including Gemini 1.5 Flash and Claude, conducting over 4,200 trials. The results indicate that the issue is systemic and requires a more robust approach to verifying agent behavior than simply refining prompts or schemas. The article emphasizes the importance of monitoring AI decision-making logic in scenarios where the rules for tool application cannot be fully formalized.