Plan-Then-Execute Agents: Implementation Notes

The evolution of autonomous software engineering has shifted toward more structured reasoning patterns, specifically the rise of Plan-Then-Execute Agents as a standard for complex refactoring and feature implementation. Unlike simple ReAct (Reason + Act) loops that decide on every individual action in isolation, these agents first generate a high-level sequence of steps before invoking any tools. This architectural separation between the "Architect" role and the "Executor" role reduces the likelihood of infinite loops and helps maintain context over long-running tasks.

Understanding the Two-Phase Architecture

The core philosophy of Plan-Then-Execute Agents is the decoupling of intent from action. In a standard autonomous loop, an LLM might decide to read a file, then search for a string, then apply a patch. Without a prior plan, the agent often loses track of the original goal if it encounters a minor technical hurdle.

The planning phase involves the agent analyzing the entire codebase context to produce a structured roadmap. This roadmap usually exists as a JSON object or a markdown list of milestones. Only once the plan is verified or accepted does the execution phase begin. This phase consumes the plan and iterates through the steps, using tools like file system access, shell commands, or browser interactions.

The Planning Phase

In the planning stage, the agent focuses on global context. It needs to identify which files are relevant and what the dependency graph looks like. This is where the agent defines the scope of work. A well-constructed plan includes:

• Clear entry points for the modification.
• Potential side effects in related modules.
• Specific validation steps for each milestone.
• An exit condition that defines success.

The Execution Phase

The execution engine is responsible for the heavy lifting. It must be resilient to errors. If a command fails during the execution of step three, the agent should not necessarily discard the entire plan. Instead, it should have a mechanism to perform a "re-plan" or a local correction. This is where tool integration becomes critical.

Managing State in Agentic Workspaces

One of the greatest challenges in implementing these agents is state synchronization. When an agent is working on a local machine, the environment state changes constantly. Files are edited, compilers are run, and processes are started. The agent needs a reliable way to observe these changes to ensure the execution phase stays aligned with reality.

In a local-first environment, the agent operates directly on your hardware. This provides low latency and high security since the code never leaves your machine. However, it also means the agent must be aware of the specific OS and shell environment. High-quality implementations often use an infinite canvas to help the developer visualize what the agent is doing in real-time.

Deska provides a unique environment for this kind of work. It allows you to run multiple agents like Claude Code or Codex CLI side by side in different panels. By using an infinite canvas, you can place a terminal next to a code editor and a browser widget, seeing the execution phase unfold across different tools simultaneously.

Feedback Loops and Validation

A plan is only as good as its verification. Effective Plan-Then-Execute Agents incorporate automated testing into their execution phase. If the plan involves refactoring a function, the execution phase should ideally include a step to run existing unit tests.

• Pre-execution check: Verify that the current environment is stable.
• Step-wise validation: Run a linter or a partial test suite after every file change.
• Final verification: Execute the full build process to ensure no regressions were introduced.

Developers can use Ask Deska to bridge the gap between their manual work and the agentic execution. Because the assistant can drive the workspace, you can ask it to open specific terminals or check the output of a background process while the agent is still running.

Comparing Agent Implementations

There are several ways to deploy these architectures. The choice often depends on whether you prefer a fully autonomous experience or a collaborative one.

For those using coding agents in a professional setting, the ability to monitor the plan as it progresses is vital. If an agent starts to deviate from the intended architecture, seeing the file changes live in a code editor allows for immediate correction.

Remote Monitoring and Mobile Integration

Modern agentic workflows are not restricted to the desk. When an agent is executing a complex, multi-step plan that might take ten minutes to complete, developers need a way to walk away without losing oversight.

Using a mobile app to monitor these agents is a growing trend. Through a secure relay, you can check the status of your terminal sessions or see if a build failed while you are away from your computer. This does not require exposing ports, as the connection is established via a direct pair between the desktop and the phone. This keeps the privacy of your local-first environment intact while providing the flexibility of the cloud.

Implementation FAQ

How to prevent plan drift in long sequences?

Plan drift occurs when the agent completes early steps but the resulting state makes later steps impossible. The best mitigation is to trigger a "re-plan" sequence whenever a tool returns an error or a validation step fails. This allows the agent to adjust the remaining milestones based on the new reality of the codebase.

Can agents run shell commands safely?

Safety is a major concern when agents have access to terminals. In a local-first setup, the agent is restricted by the permissions of the user running the application. It is recommended to use agents that show you the command before execution or to run them in a dedicated workspace where you can monitor the output in real-time.

Why use an infinite canvas for agents?

An infinite canvas helps manage the cognitive load of watching an agent work. Instead of flipping through tabs, you can arrange the agent terminal, the logs, and the modified source code in a single spatial view. This makes it much easier to spot when an execution step is going wrong.

Getting Started with Local Agents

Implementing your own Plan-Then-Execute workflow starts with choosing the right tools for your environment. If you value data sovereignty and speed, a local-first approach is usually the best path forward. You can experiment with different agents and see how they interact with your specific stack by setting up a dedicated workspace.

You can download the Deska desktop app for Mac, Windows, or Linux to start running multiple agents side by side. Whether you use your own API keys for a lifetime experience or choose managed inference, the ability to see your agents work on an infinite canvas will change how you approach complex software engineering tasks. For more details on configuring your environment, check the settings and getting started documentation.