Methods, metrics, and approaches for evaluating AI agents, ensuring quality, and continuous improvement.
AI systems rarely fail abruptly in production. Instead, they degrade gradually-through drift, decay, and compounding errors. This knowledge item explains how quality erosion happens at scale and how to design evaluation mechanisms that detect and contain it early.
Human oversight is essential for trustworthy AI-but when applied indiscriminately, it destroys scale and speed. This knowledge item explains how to design human-in-the-loop mechanisms that preserve control and judgment without turning people into bottlenecks.
Most AI teams collect metrics-but few use them to drive decisions. This knowledge item explains how to design AI quality metrics that trigger concrete actions, enabling reliable control, accountability, and continuous improvement in production systems.
Evaluation in agentic systems cannot rely on static tests or post-hoc reviews. This knowledge item explains how to design evaluation loops as first-class architectural components-ensuring AI systems remain reliable, measurable, and aligned with business intent over time.
Many AI systems appear successful during pilots but quietly fail in production. This knowledge item explains why evaluation breaks down after deployment-and how organizations must rethink evaluation as an architectural capability, not a final checkpoint.