The core issue is a discrepancy between the agent's internal optimization metrics (cost, shipping time) and the actual business outcome (customer satisfaction). The high volume of complaints about delays indicates the agent's definition of "on-time" is not aligned with customer expectations. Therefore, prioritizing the metric that directly measures the customer experience—the percentage of deliveries within an acceptable delay window (C)—is crucial. This metric quantifies the problem from the customer's perspective and provides a clear target for re-aligning the agent's optimization goals.

A. Demand forecasting is a related but separate issue; the current problem is with delivery execution, not predicting order volume.

B. Focusing on cost savings ignores the negative customer impact, which could lead to greater long-term financial loss through customer churn.

D. The agent's adherence to its own schedules is the problem; the schedules themselves are flawed, making this a misleading metric of success.

1. Saleh, I. (2021). "Responsible AI: A Human-Centered Approach to Designing and Engineering AI Systems". O'Reilly Media. Chapter 4, "Evaluating AI Systems," emphasizes the importance of aligning system metrics with human-centric outcomes and business KPIs, not just internal performance goals.

2. Stanford University, CS 221: Artificial Intelligence: Principles and Techniques. Lecture on "Machine Learning: Evaluation Metrics". The courseware stresses selecting metrics that reflect the true goal of the system, highlighting the danger of proxy metrics that diverge from the desired outcome (e.g., optimizing for clicks instead of user satisfaction).

3. NVIDIA. (2023). "AI for Supply Chain Optimization". Whitepaper. Discusses using AI to improve outcomes like on-time-in-full (OTIF) delivery, a customer-centric metric, rather than focusing solely on internal cost or speed metrics.