An Agent Run Is Not Done When the Model Stops Talking
The article argues that an AI agent run should not be considered complete simply because the model has stopped generating tokens. True completion requires verification that the task was fully and correctly executed, with clear evidence and reproducibility. The author calls for production-grade infrastructure to track agent runs with the same rigor as traditional job systems.
- ▪An agent run is not complete just because the model stops emitting tokens.
- ▪Current agent systems often fail to verify if tasks are truly finished or if the model hit a limit or error.
- ▪Production job systems like Airflow and Kubernetes track execution lifecycle states, which agent systems should emulate.
- ▪For an agent run to be considered done, it must be possible to verify clean exit, task completion, evidence for claims, and exact reproducibility.
- ▪The lack of structured state tracking and exit codes in agent systems undermines reliability and trust in production environments.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3908201) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Jeremy Blankenship Posted on May 1 • Originally published at jeremyblankenship.dev An Agent Run Is Not Done When the Model Stops Talking #ai #agents #infrastructure An Agent Run Is Not Done When the Model Stops Talking The Problem You prompt an agent. It runs. Tokens stream out. It stops. You read the output. Done. Except you have no idea if it's done. When you run an AI agent on a real task, the model producing output is the easiest part.
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