Introducing Agent Evals: Score your agents on real outcomes
Blog post from Inngest
Agent Evals by Inngest introduces a novel approach to evaluating AI agents by focusing on real-world outcomes rather than just the appearance of success. This new system leverages APIs that integrate directly into codebases, allowing for the measurement of outcomes like customer retention and conversion rates, which are not immediately visible after an agent's task completion. It includes features like Experiments, Scoring, and Defer, which enable users to run variant tests, attach meaningful metrics to outcomes, and manage follow-up tasks, respectively. The tool aims to bridge the gap in current observability systems that only evaluate if the code executed correctly without determining if it achieved the desired business results. By incorporating outcome-based scoring directly into the execution layer, Inngest enables more accurate assessments and adjustments in AI models, providing a more reliable means of determining effectiveness. This approach not only enhances model observability but also aligns technical performance with business objectives, ensuring that agents contribute positively to the bottom line.
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