In Oracle AI Agent Studio, the evaluation framework is designed to assess an agent's performance across multiple dimensions. The "Security" category of metrics is specifically focused on the agent's safety and resilience against malicious use. This category includes evaluations for prompt injection, which tests the agent's ability to resist attempts to bypass its core instructions, and content safety, which measures whether the agent's outputs adhere to safety guidelines and avoid generating harmful or inappropriate content. These two metrics are crucial for ensuring the agent behaves securely and responsibly.

A. User and Token count: These are categorized as usage and cost metrics. They measure operational aspects like user engagement and resource consumption, not the agent's security posture.

C. LLM requests and latency: These are performance and cost metrics. They evaluate the agent's responsiveness and the operational load on the underlying language model, not its security integrity.

1. Oracle Cloud Infrastructure Documentation - Generative AI Agents: In the section on evaluating an agent version

the documentation explicitly lists the categories of metrics provided in the evaluation results. It states

"The evaluation results provide metrics for the following categories: ... Security: Evaluates the agent's security against prompt injection and content safety." This directly confirms that prompt injection and content safety are the security evaluation metrics. The document also lists "Cost and latency" as a separate category

which includes metrics like token count and latency

confirming options A and C are incorrect categories for security.

Source: Oracle Cloud Infrastructure Documentation

"Evaluating an Agent Version

" Section: "Reviewing Evaluation Results." Document accessed October 2023. (Available at: https://docs.oracle.com/en-us/iaas/generative-ai/generative-ai-agents/using/agent-evaluate.htm)