LLM agents can fail, and their actions can have real consequences. They need condition monitoring to detect anomalous behavior accurately and promptly. Decision models, specially trained AI models that take relevant context and make quick judgments, make real-time agent anomaly detection much more feasible.
The idea is straightforward: continuously monitor the agent as it works. Whenever a new tool interaction completes, we give the decision model the accumulated history of tool calls and results. The model assesses the execution so far and returns an updated judgment of whether it's normal or anomalous.
Meanwhile, the agent continues working independently. This is the so-called decision-model-as-a-monitor pattern. And it is what we will build in this post.
Specifically, we will create an agent with the Open AI Agents SDK and connect an independent decision-model monitor. We will run two versions of the same task—one with ordinary behavior and one with an anomaly—and see whether the monitor correctly distinguishes them as each execution unfolds. The complete notebook is included.
LLM agents can fail, and their actions can have real consequences. They need condition monitoring to detect anomalous behavior accurately and promptly. Decision models, specially trained AI models that take relevant context and make quick judgments, make real-time agent anomaly detection much more feasible.
The idea is straightforward: continuously monitor the agent as it works. Whenever a new tool interaction completes, we give the decision model the accumulated history of tool calls and results. The model assesses the execution so far and returns an updated judgment of whether it's normal or anomalous.
Meanwhile, the agent continues working independently. This is the so-called decision-model-as-a-monitor pattern. And it is what we will build in this post.
Specifically, we will create an agent with the Open AI Agents SDK and connect an independent decision-model monitor. We will run two versions of the same task—one with ordinary behavior and one with an anomaly—and see whether the monitor correctly distinguishes them as each execution unfolds. The complete notebook is included.
Source: Towards Data Science · Summarized by HeadlinesBriefing