AI evals for agents: what they are and how to write them (www.evalshift.dev)

Recent advancements in evaluating AI agents have introduced "AI evals," which provide a structured way to assess the performance of AI systems in a holistic manner. Unlike traditional LLM evaluations that focus on a single response, AI evals grade a complete run of an agent, analyzing its interactions, tool calls, and final outputs. This guide outlines how to create effective agent evals, emphasizing the importance of defining success based on the outcome rather than just the agent's claims. It also highlights the distinctions between capability and regression evaluations, which are crucial for understanding an agent's performance over time. The significance of these evals lies in their potential to improve AI model assessments, ensuring that agents respond correctly while adhering to operational rules. The guide provides practical steps and best practices for creating evals, including the use of multiple grader types—code, model-based, and human—to capture a spectrum of evaluation criteria. By systematically running multiple trials and focusing on the underlying traces, developers can better identify regressions and enhance the reliability of AI agents in real-world applications. This multifaceted approach aims to mitigate risks associated with AI inaccuracies, ultimately leading to more effective and trustworthy AI systems.