息をするように大量のAIを同時稼働させる時代。管理が面倒すぎたので「AI開発用OS」を自作したManaging multiple simultaneous AI agents (Cursor, Claude Code, Cline) across…

A developer working extensively with AI coding agents has documented an increasingly common problem: as tools like Cursor, Claude Code, and Cline proliferate across a workflow, keeping them coordinated becomes a management burden in itself. Writing on Qiita, the author describes building a personal "AI development OS" to centralize orchestration of these agents, and shares the design thinking behind it. The account matters because it reflects a broader shift in how software is being written, where the constraint is no longer generating code but supervising many autonomous processes at once.

The starting point is a specific operating style. The author runs local development with a "one Claude Code per project" convention, meaning each active project gets its own dedicated agent instance. That approach keeps context clean and prevents one project's state from bleeding into another. But it scales poorly in the obvious way: with several projects open, the developer ends up juggling multiple agents, multiple terminals or editor windows, and multiple streams of output that all demand attention. The friction the author reports is not about any single tool failing, but about the cumulative overhead of running them side by side.

To address that, the author built what they call an OS for AI development. The phrasing is metaphorical rather than literal; this is not a replacement operating system but a coordinating layer that sits above the individual agents. Based on the framing, it appears to function as an orchestration and management environment that gives a unified view of the agents in play, standardizes how they are launched and monitored, and reduces the manual context-switching that comes with the per-project model. The post presents both the design philosophy and the implementation, suggesting the author treats the setup as a reusable system rather than a one-off script.

Some context on the tools helps explain why this problem arises now. Cursor is an AI-first code editor built on a VS Code foundation, designed to keep a model closely integrated with the working codebase. Claude Code is Anthropic's agentic command-line tool that can read, edit, and run code across a repository with relatively little hand-holding. Cline is an open-source agent that operates inside VS Code and can execute multi-step tasks. Each is capable of running semi-autonomously, which is precisely what makes parallel operation attractive and, at the same time, hard to supervise. When a single developer can spawn several agents that each act on their own, the human role shifts toward dispatching work and reviewing results rather than typing every change.

This individual effort sits alongside a wider industry movement toward multi-agent workflows. Techniques such as Git worktrees, which let a developer check out several branches simultaneously, and terminal multiplexers like tmux are frequently used to isolate parallel agent sessions. Container-based sandboxes are another common pattern for giving each agent a contained space to operate. Tool vendors have been moving in a similar direction, adding background agents, task queues, and orchestration features intended to manage more than one autonomous process. The author's homemade system can be read as a grassroots response to the same pressure that commercial products are beginning to target, built to fit one person's particular habits.

Because the post is a personal blog entry rather than a released product, its claims should be read as one practitioner's experience rather than a benchmarked or generalizable solution. The design that works for a "one agent per project" style may not translate cleanly to teams, shared infrastructure, or different toolchains, and the write-up does not appear to position itself as a finished framework for others to adopt wholesale. Still, the underlying observation is likely to resonate: as agentic coding tools become more capable and easier to run in parallel, the bottleneck migrates from writing code to coordinating the systems that write it. Whether developers solve that with custom tooling, emerging vendor features, or established Unix building blocks, the need for a coherent management layer over many concurrent AI agents is becoming a recognizable and recurring theme in local development.