Offline evaluation for AI agents: Best practices

Offline evaluation is a crucial practice for developing reliable LLM-powered applications and agents, as it allows teams to test changes against known scenarios before deploying them to production. This method helps identify potential issues early on, reducing the risk of user frustration and revenue loss. Offline evaluation involves using curated datasets with annotated test cases that cover core use cases and potential edge cases, allowing developers to benchmark and iterate on AI agents efficiently. A robust evaluation framework includes data, tasks, and evaluators: data consists of annotated test cases, tasks involve the logic that produces outputs, and evaluators measure the quality of those outputs. Such a framework helps ensure that agents perform reliably by comparing different versions and catching regressions before they affect end users. Datadog's LLM Experiments provides tools for creating and managing these components, enabling developers to perform offline evaluations and improve AI agents with precision, scalability, and reduced incident rates.

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