AI Coding is now entering a particularly intriguing phase. Previously, discussions centered primarily on model capabilities, context window length, Agent Loops, Tool Use, and automated programming. However, once Agents are deployed into real-world development environments for extended periods, many teams have discovered that the core challenge is no longer just about "whether code can be generated," but rather "whether the system can manage the Agent's entire runtime lifecycle." Once an Agent runs continuously for dozens of minutes or even hours, the workspace evolves constantly: shells execute commands, files are modified frequently, tool calls accumulate, and eventually, the entire project enters a highly typical state: the code has changed, but no one knows exactly why or how it ended up in its current form. Many current AI Coding products suffer from this issue. You can see the final output, but you lack visibility into the complete process. It is difficult to determine which step modified a specific file, which prompt generated a particular piece of code, which execution introduced a bug, or when the workspace started becoming corrupted. Replaying the entire execution chain is also challenging. Human developers have Git, but AI Agents currently lack a truly mature runtime-level version control system. Recently, more and more teams have begun rethinking Agent Infrastructure, essentially aiming to add a layer of "software engineering infrastructure" for Autonomous Agents. Truly mature Agent systems in the future will likely possess capabilities such as execution DAGs, workspace snapshots, session timelines, tool tracing, persistent history, replay, time travel, and audit logs. This is because the next phase of AI Coding competition is no longer solely about code generation capabilities, but about whether the system possesses traceable, recoverable, auditable, and replayable runtime capabilities. Essentially, the goal is to equip the system with traceability, recoverability, and auditability. I recently came across a very interesting open-source project: https://github.com/regent-vcs/re_gent... It does something very direct: it adds a layer of version control and observability infrastructure for AI Agents, allowing you to trace exactly which line of code was generated during which Agent execution. I believe this direction will become increasingly important. Many people still view AI Coding as a "smarter Copilot," but the entire industry is actually evolving towards "Autonomous Software Systems." The next stage of AI Coding is no longer just about model capabilities. It is more akin to reinventing a set of software infrastructure tailored for Autonomous Agents.