The agent's primary requirements are to be "accurate" and "quick." "Factual correctness" directly measures accuracy, which is paramount for compliance-related questions where incorrect information can have serious consequences. This involves verifying the agent's responses against a trusted source of truth. "Latency" is the metric for speed, measuring the time from query submission to response delivery. Low latency is crucial for a good user experience and for applications that require rapid decision-making. These two dimensions directly map to the core functional requirements of the agent.

C. Screen brightness is a display hardware setting and is entirely unrelated to the agent's performance or validity.

D. Keyboard layout is a user input hardware configuration and has no bearing on the agent's processing or response quality.

E. Color palette preference is a user interface (UI) design choice that affects aesthetics, not the agent's functional correctness or speed.

1. NVIDIA NeMo. (2024). NeMo Evaluator Documentation. The documentation details metrics for evaluating LLMs, including "correctness" (assessing factual accuracy against ground truth) and performance metrics like "latency" and "throughput." (Section: "Model Evaluation Metrics").

2. Gao, L., et al. (2023). Enabling Large Language Models to Generate Text with Citations. This paper on RAG evaluation emphasizes two key axes: "Faithfulness/Factual Consistency" (correctness) and "Relevance," while also considering performance metrics like response time (latency). (Section 4: "Evaluation").

3. Stanford University. (2023). CS25: Transformers United. Lecture on "LLM Evaluation." The course outlines key evaluation criteria for LLMs, highlighting "Accuracy" (correctness on specific tasks) and "Efficiency" (including latency and computational cost) as fundamental pillars.