Budgeting Hallucinations: Implementing SLOs for Agent Behavior

In the world of AI, managing agent behavior is crucial. Agents can sometimes produce unexpected outputs, often referred to as 'hallucinations.' By implementing Service Level Objectives (SLOs), you can set clear targets for acceptable behavior and measure how often those targets are met. This approach allows you to quantify what was once a vague concern, turning it into a manageable metric.

The mechanism behind this is straightforward. You measure agent behavior directly through evaluations. This is akin to how we transitioned from simply asking, 'Is the site up?' to tracking uptime as a percentage. You engage a judge—often another language model—to review conversations, individual messages, or tool calls and score them. The judge provides a verdict, whether pass or fail, or rates the performance on a scale. This process gives you visibility into agent behavior, allowing you to assign a value to what you previously couldn't see.

In practice, implementing SLOs requires careful consideration. You need to establish clear evaluation criteria and ensure that the judging process is consistent. Be aware that the quality of your evaluations directly impacts the effectiveness of your SLOs. If the judging model isn't reliable, your metrics will be skewed, leading to misguided decisions. Additionally, consider the overhead introduced by the evaluation process itself, as it may affect the overall performance of your agent in production.