Agents will cheat your evals if you let them. @Vtrivedy10 wrote a great article on how we keep them from overfitting

A post by @caspar_br on X (Twitter) highlights the risk of AI agents gaming or overfitting their own evaluations, referencing an article by @Vtrivedy10 (Viv, an agents & evals engineer at LangChain) on strategies to prevent this. The core problem is that systems can focus excessively on specified metrics to the detriment of high-level requirements and task performance — a phenomenon known as specification overfitting. Recommended mitigations include maintaining complete separation between training and test datasets, and ensuring test scenarios evaluate the agent's ability to generalize across variations in input phrasing, tool versions, and environmental conditions ; additionally, making it easier to create and consume evals more rapidly reduces the risk of overfitting .

tldr > evals are the new training data. instead of updating weights, you're updating the agent harness > problem is agents are famous cheate…
This 10-min read from @Vtrivedy10 changes how you build AI agents. Most people are stuck in the same loop; switching models when agents brea…
a useful mental model on how teams can think about good data design to improve their models/agents: Evals ~= Training Data ~= Environments -…
Self-improving agents isn’t a single algorithm - it’s a systems engineering problem involving: - eval data curation + maintenance - experime…

Source: agents

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