Agent ATO: Visualizing Agent Interaction Timelines from Logs

2026-09-08

AI Summary

为了解决AI编码代理行为难以理解的问题，本文提出Agent ATO工具，通过可视化控制台日志中的代理交互时间线来帮助开发者检查和比较代理的行为。

Abstract

AI coding agents are becoming part of developers'workflows, but their behavior is difficult to understand from final code changes alone. During a task, agents interact with software repositories through sequences of actions such as searching for files, reading code, editing programs, and running tests or build commands. These interactions, together with token usage, are often recorded in console logs, but raw logs are difficult for developers to inspect. In this paper, we propose Agent ATO (Agentic Trajectory Observer), a tool for visualizing AI coding agent interaction timelines from console logs. Agent ATO extracts agent interactions, classifies them by command or tool type, and visualizes them as timelines. In addition to an all-interaction timeline, Agent ATO provides filtered timelines that emphasize file discovery, file reading, file editing, and execution while preserving surrounding context. We illustrate how Agent ATO may help developers inspect and compare agent actions using selected runs from two repair tasks. Future work will apply Agent ATO to more agents, tasks, and development environments, and will evaluate whether it reduces the effort needed to compare trajectories.

Problem

• AI coding agents
• developer workflows
• interaction timelines
• console logs
• agent behavior

Innovation

• Visualizing Agent Interaction
• Console Logs
• Timeline Visualization
• AI Coding Agents