Using in-the-loop evals is beneficial when the tolerance for mistakes is low, although it may increase time and costs.

Having a human in the loop improves results by allowing agents to ask clarifying questions, leading to better outcomes than relying solely on initial prompts.

Online evals assess the app's performance in real-time using actual production data, allowing for immediate tracking of performance.

The AI agent receives feedback from the language server and reacts to it like a real user, ensuring it doesn't go off the rails.

There are three types of evaluations: offline evals, online evals, and in-the-loop evals, each serving different purposes in the evaluation lifecycle.

Independently evaluating sub-agents helps identify weak links and improve overall accuracy in the system.

AI agents may evolve to close feedback loops without human intervention, but they currently lack the instinct to recognize and correct their own errors, which is a significant barrier to their effectiveness.

Offline evals involve running your app against a dataset before production to measure performance and track changes over time.

Testing AI agents involves pre-deployment testing with curated conversation sets to ensure expected responses, followed by ongoing evaluation to adapt to changing user interactions and questions.