The 5 Levels of Running Coding Agents

 

I have classified coding using AI agents into five levels, shifting the focus from simple prompting to the logistical challenges of infrastructure and environment management.

The 5 Levels of Agent Management:

1. Level 1: The IDE: Starting with a single agent inside editors like Cursor or Copilot. While easy to use, it can lock you into specific proprietary workflows.
2. Level 2: The Terminal: Running agents as direct processes in the terminal. This offers more control and flexibility by decoupling the agent from a specific IDE.
3. Level 3: Multi-Agent Management: Scaling up to multiple agents simultaneously. Tools like T-Mux or, more specifically, Herder to manage background sessions and track agent status.
4. Level 4: Remote Machine: Moving agents off your local machine to a desktop or server using Tailscale and SSH. This allows for GPU-intensive tasks and ensures agents continue running when you close your laptop.
5. Level 5: The Cloud: Running agents in disposable cloud sandboxes, such as Control Plane. This provides total environmental control, persistence, and the ability to connect agents to internal infrastructure securely.

Level 1

Level 1 represents the starting point for most users, which is running a single AI coding agent directly inside your IDE. Common examples include tools like Cursor or Copilot or Claude Code or Codex integrated into your code editor.

Key characteristics of this level include:

• Workflow simplicity: You provide a task, the agent modifies files, and you review the resulting "diff" before proceeding.
• Ease of use: It is a highly effective way to "vibe code" and complete a significant amount of work without needing additional configuration.
• Constraints: While convenient, the IDE often dictates the agent's workflow, how you interact with it, and what tools are exposed. Additionally, it frequently involves extra subscription costs on top of the base model fees

Level 2

Level 2 shifts the workflow from the IDE to the terminal. By treating the AI agent as a standalone process rather than a plugin, you gain greater control over the environment and avoid proprietary limitations.

Key advantages of this approach include:

• Flexibility: You can choose your preferred model, specific machine, and custom environment tools without being locked into a single editor's ecosystem.
• Automation: It becomes significantly easier to script interactions, chain multiple tools together, or move your entire setup via SSH to different machines.
• Open Ecosystem: This level often relies on open-source tools such as OpenCode or Claude via command-line interfaces which can be more cost-effective than proprietary IDE subscriptions.

Level 3

Level 3 focuses on the challenges of managing multiple AI agents simultaneously. As you scale your projects, you will likely find yourself needing to run several agents at once, which can quickly lead to terminal clutter and difficulty tracking the status of each task.

Key tools for this level include:

• T-Mux: A traditional terminal multiplexer that allows you to keep multiple sessions alive in the background. It is effective for managing numerous processes through different panes, enabling you to detach and reattach to your workflows easily.
• Herder: A modern alternative to T-Mux specifically designed for agent management. Herder solves the "mental overhead" of tracking which agents are active, waiting for input, or have completed their tasks, making it a more purpose-built solution for this workflow.

Ultimately, this level is about moving from manually juggling terminal windows to utilizing specialized software that organizes your agent fleet, preventing the chaos of having dozens of processes running without clear oversight.

Level 4

Level 4 focuses on decoupling your coding agents from your local hardware. As you scale, you may run into limitations like needing a more powerful GPU for machine learning or simply wanting your agents to continue working without being tied to your laptop.

Key strategies for this level include:

• Remote Access via SSH: By setting up your secondary machine (e.g., a desktop) and your laptop on the same Tailscale network, you can remotely control your powerful machine from anywhere.
• Background Persistence: Using T-Mux or Herder on a remote machine ensures that your agents keep running even if you close your laptop.
• Simplified Workflows: Using the remote flag in Herder to handle the SSH connection automatically, making it seamless to manage your agent fleet across different physical devices

Level 5

Level 5 moves the entire agent infrastructure away from physical hardware and into disposable cloud environments. This stage focuses on gaining maximum control while eliminating dependencies on your local machine.

Key features of this level include:

• Environmental Control: Unlike vendor-managed solutions (like Cursor or Claude's built-in cloud agents), using tools like Control Plane allows you to define custom images containing your specific dot files, tools, and preferred agent workflows.
• Persistence and Flexibility: You can spin up a sandbox in under a minute, hand it tasks to run uninterrupted, and then suspend the sandbox when idle to reduce compute costs to zero, resuming exactly where you left off later.
• Secure Infrastructure Access: A major advantage over IDE-based cloud agents is the ability to securely connect your sandbox to internal infrastructure (e.g., AWS, GCP, or Azure) using identity management, enabling the agents to interact with your private services without compromising security.