Why Agent Tasks Take Longer Than Expected

The rise of autonomous coding assistants has introduced a new form of technical overhead that many teams are just beginning to quantify. While these tools promise to handle complex refactors and feature implementations, many developers find that agent tasks take longer than expected because of a phenomenon known as planning drift. This occurs when an AI agent loses the logical thread of a multi step objective, leading to circular reasoning, hallucinated file paths, or repetitive attempts to fix a single failing test. Understanding why this happens requires a deep look into how large language models interpret intent versus state.

The Anatomy of Planning Drift

When a developer starts an agentic workflow, they are essentially handing over the "loop" to a model. The model must analyze the codebase, plan a series of actions, execute those actions, and verify the result. The delay usually happens in the verification and correction phases. Unlike a human who might realize a specific approach is a dead end after two minutes, an agent might exhaust its context window trying to force a solution that contradicts the project architecture.

There are several technical bottlenecks that contribute to this time inflation. First is the latency of tool calling. Every time an agent needs to list files or read a specific line of code, it triggers a round trip to the model. Second is the lack of persistent environmental awareness. Many agents operate in a vacuum, losing the visual or structural context of the wider project unless it is explicitly fed back into the prompt.

The Problem of Context Saturation

As an agent works, the conversation history grows. This history includes every command output, every file read, and every error message. Eventually, the most important information, the original goal, gets pushed further away from the model focus. When the context window becomes saturated, the agent begins to make "shallow" decisions. It fixes the immediate error but breaks a global dependency, leading to a cycle of regressions that extends the task duration significantly.

Comparing Agentic Architectures

Different tools handle this drift in various ways. Traditional CLI based agents often focus on a linear execution model, which is efficient for small fixes but prone to getting lost in large repositories. Integrated development environments (IDEs) attempt to solve this by providing more metadata about the workspace, yet they still struggle with the "black box" nature of long running tasks.

The industry is moving toward environments that treat the agent as a first class citizen of the workspace. Tools like Deska provide an infinite canvas where you can place terminal panels, code editors, and agent threads side by side. This allows a developer to monitor the agent progress in real time without switching tabs, which is a critical factor in catching drift before it consumes an entire afternoon.

How Deska Mitigates Estimation Drift

Deska takes a local-first approach to agentic development, keeping your code and sessions on your machine. This reduces the latency often associated with cloud based tools and ensures that the agent has direct access to the local file system. By running coding agents like Claude Code or OpenCode in dedicated panels, the user can observe the step by step execution.

If an agent starts to loop, a developer using Deska can intervene immediately. You can use Ask Deska to run a command or open a specific file to provide the agent with the missing context. This hybrid approach, where the human provides the high level guardrails while the agent handles the boilerplate, helps ensure that agent tasks do not exceed their predicted timeframes.

Visual Monitoring and Multi Agent Workflows

One of the biggest reasons agent tasks take longer than expected is the inability to see the "big picture." In a standard terminal, you only see the current output. In Deska, you can zoom out to see the agent threads next to the browser widgets showing the live application. This spatial arrangement makes it obvious when an agent is stuck in a loop. You can even run multiple agents side by side to compare their approaches to the same problem, choosing the one that reaches a valid solution first.

Strategies for Better Estimation

To improve the accuracy of your task estimations when using AI, consider the following practices:

• Break down the prompt into smaller, verifiable units of work rather than one large objective.
• Monitor the terminals closely during the first three minutes of execution to ensure the agent has mapped the file structure correctly.
• Use a dedicated notes notebook to keep track of what the agent has tried, providing a "paper trail" that survives context resets.
• Set a hard limit on the number of iterations an agent can perform before it must ask for human clarification.

For developers who need to step away from their desks, the Deska mobile app allows for remote monitoring. You can check the status of a long running refactor from your phone via a secure relay. If you see the agent has stalled or is throwing repetitive errors, you can pause the session or send a corrective prompt to get it back on track.

FAQ

How to stop AI agents from looping?

To stop an agent from looping, you must provide new information that breaks the circular logic. This often involves manually correcting a file path, providing a missing environment variable, or explicitly telling the agent to ignore a specific failing test that is a red herring. Using a workspace that shows code and git files alongside the agent helps identify these blockers quickly.

Why is my coding agent so slow?

Slowness is typically caused by high API latency or an overly large context window that forces the model to process too much data for every turn. Optimizing your settings to include only relevant files or switching to a faster model provider through pricing tiers can help improve response times.

Can I run multiple agents at once?

Yes, modern canvas based workspaces allow you to run several coding agents simultaneously. This is useful for "race" testing solutions or having one agent write tests while another writes the implementation. Each agent operates in its own panel, maintaining its own state and command history.

Getting Started with Reliable Agent Workflows

Reducing the gap between expected and actual task time is about visibility and control. By moving away from hidden processes and adopting a visual, local-first environment, you can harness the power of AI without losing hours to planning drift. You can explore the full range of panels and features by downloading the app for your preferred operating system.

Start building more predictably today. Download Deska and take control of your autonomous workflows.