The Deska blog
When the Agent Is Slower Than Just Doing It
Explore why the agent is slower than just doing it in AI coding, and how a better workspace architecture can fix the overhead of autonomous workflows.
12 min read
The feeling of frustration is familiar to any developer who has integrated AI into their workflow: you prompt a tool to perform a refactor, only to watch it spin through loops of file discovery and syntax errors. In many scenarios, the agent is slower than just doing it yourself because the overhead of context gathering and iterative correction outweighs the time a human takes to make a surgical edit. We are currently in a transitional phase where the raw intelligence of large language models (LLMs) often outpaces the efficiency of the interfaces we use to control them.
The Cognitive Tax of Supervision
When you use an autonomous coding agent, you are not just delegating a task. You are taking on the role of a supervisor. This role requires you to verify every file the agent reads, every command it executes, and every diff it proposes. If the task is a simple search and replace across three files, the time it takes to initialize the agent, wait for it to index the project, and then approve its plan is significantly higher than using a global find and replace in a standard editor.
This bottleneck is often referred to as the "babysitting problem." You cannot fully walk away because agents can hallucinate or get stuck in recursive loops. If you have to watch the terminal output to ensure the agent does not delete a critical directory, the promise of automation starts to dissolve. The speed of the agent is limited by your ability to verify its output in real time.
Why Latency and Context Windows Matter
The technical reality of why the agent is slower than just doing it often comes down to the round trip time of API requests and the way context is managed.
- Model Latency: High reasoning models are slow. Waiting 30 seconds for a complex reasoning chain is acceptable for a whole feature but painful for a small bug fix.
- Context Loading: Agents must often "read" your codebase by listing directories and reading files one by one. This iterative process creates multiple network calls, adding seconds or minutes to the start of a task.
- Token Limits: If an agent consumes too much context, the cost rises and the model might become less accurate, leading to "lazy" coding where it provides comments instead of actual code.
In contrast, a developer has the entire mental model of the project loaded in their head. You know exactly where the AuthService lives. You do not need to ls the directory to find it.
Comparing Agentic Workflows
Different tools take different approaches to solving these speed issues. It is important to understand which tool fits which task to avoid the trap of using a slow agent for a fast job.
| Tool Approach | Strength | Weakness |
|---|---|---|
| Terminal Based Agents | Direct access to CLI and file system | Hard to visualize multiple changes at once |
| IDE Plugins | High awareness of open files | Limited to the editor's UI constraints |
| Infinite Canvas | Visualizes multiple agents and panels | Can become cluttered without organization |
| Chat Only | Low friction for quick questions | Cannot execute code or verify changes |
Tools like Claude Code or Codex CLI are powerful because they operate directly on your files, but they can be opaque. Other tools focus on keeping you in the loop by showing you exactly what is happening in a side-by-side view.
The Infrastructure of Efficiency
To make agents faster, the environment must reduce the friction of data movement. This is where local-first architectures become critical. If the workspace has to upload your entire repository to a cloud server before an agent can even look at it, you have already lost the productivity race. A tool that runs agents locally or uses a secure relay to communicate directly with your machine preserves the speed of your local disk.
Another factor is visibility. If an agent is working in a hidden background process, you have no way to intervene until it fails. By using an infinite canvas, you can place the agent's terminal next to the code editor and a live browser preview. This spatial arrangement allows you to spot a mistake as it happens, rather than waiting for a finished, broken PR.
Integration with Deska
Deska addresses the "slower than doing it" problem by treating agents as modular components within a flexible workspace. Instead of a single chat box, you can run Claude Code, Codex CLI, and OpenCode as distinct panels side by side.
When you use Ask Deska, the assistant does not just write code. It can drive the workspace by opening terminals or arranging your environment. If the agent starts to drift, you can instantly take over in the Monaco-based code editor or the integrated terminal. This hybrid approach ensures that the agent handles the heavy lifting while you handle the precision work without switching windows.
Key features that help bridge the speed gap include:
- A mobile app that lets you monitor long running agent tasks through a secure relay without exposing ports.
- A BYOK model that lets you use your own API keys for direct control over model selection and costs.
- Spatial organization where coding agents and notes exist in the same visual field.
Strategic Abandonment: When to Stop Using the Agent
Part of being an efficient developer in the AI era is knowing when to turn the agent off. If you find yourself correcting the same syntax error for the third time in an agent's loop, the agent is officially slower than just doing it.
You should likely switch to manual coding when:
- The task involves complex UI polish that requires "feel."
- The agent is struggling with a library version mismatch it cannot resolve.
- The time spent explaining the prompt exceeds the time to type the code.
By using a workspace that supports data and storage locally, you can quickly pivot from an autonomous agent session back to manual editing without losing your context or your place in the project.
FAQ
Why is my AI agent stuck in a loop?
This usually happens when the agent lacks sufficient permissions to read a file or when the feedback from a failing test is ambiguous. The agent keeps trying the same logical path because it does not realize the environment is blocking its success. Checking your settings and permissions can often resolve this.
How to speed up AI coding agents?
To increase speed, provide a narrower context. Instead of letting an agent scan the whole project, point it to specific files or use a workspace that allows you to isolate code and git files. Reducing the number of steps the agent needs to take to find information will significantly cut down on latency.
Is Claude Code better than other CLI agents?
Different agents excel at different things. Claude Code is known for strong reasoning and a conversational interface, while others might be more specialized for specific languages. The best approach is to run them side by side in a unified workspace to see which one handles your specific stack more efficiently.
Optimize Your Workflow Today
The goal of developer tools is to remove barriers, not add them. If you find that your current setup makes the agent feel like a burden, it might be time to rethink your environment. Deska provides the flexibility to use agents when they work and get them out of the way when they do not.
Download Deska for Mac, Windows, or Linux and start building in a workspace designed for the way you actually work.