How Four Types of AI Coding Agents Differ When They Use the Same LLM

When using AI coding agents, a common question arises: if different tools call the same underlying large language model (LLM), why can their response speed, number of actions, answer length, and final results vary so widely?

There is no contradiction. An LLM is only the reasoning engine within a larger system. A coding agent also includes a task planner, tool caller, context manager, permission controller, result formatter, and user interface. Even when the underlying model is identical, different prompt designs, available tools, execution workflows, and stopping conditions can create very different user experiences.

This article compares four common categories of AI agents at an abstract level:

1. Graphical AI coding agents
2. Extensible open agent frameworks
3. Terminal-based research agents focused on retrieval and analysis
4. Terminal-based coding agents focused on code changes and project operations

The discussion does not focus on specific products. Instead, it examines the technical mechanisms and practical workflows behind their differences.

1. The Same Model Does Not Mean the Same Capability

A complete coding agent typically includes several layers:

1. Base model layer: Understands natural language, produces plans, writes code, and explains results.
2. Agent orchestration layer: Determines when the model calls tools, whether tasks are decomposed, and when execution stops.
3. Tool layer: Includes file reading, code search, terminal execution, web access, version control, and testing tools.
4. Permission layer: Determines which actions require user approval and which can run automatically.
5. Context layer: Provides the model with project structure, action history, command output, and error messages.
6. Output layer: Organizes the execution process into chat responses, patches, files, or reports.

As a result, evaluating an agent requires more than asking whether the model produced a good answer. It is also necessary to assess whether the agent selected the right tools, controlled the execution scope, handled failures, and produced reproducible results.

For example, consider the same model receiving the task: “Analyze this project and fix the failing tests.”

• Agent A may read only a few files and immediately suggest changes.
• Agent B may scan the directory, search for relevant functions, run tests, and produce a patch.
• Agent C may conduct multiple rounds of research and generate a detailed analysis report.
• Agent D may repeatedly request authorization to ensure that every operation is approved.

These differences primarily come from the agent workflow rather than from the underlying model.

2. The Four Agent Categories Have Different Core Goals

2.1 Graphical Coding Agents: Easy to Adopt and Focused on Visual Control

Graphical coding agents are typically integrated into an editor or desktop environment. Users can inspect files in windows, review edits, and accept or reject changes. Their lower learning curve makes them useful for developers who frequently need to examine code diffs.

Common characteristics include:

• Changes are usually displayed in a diff view.
• Users can approve file changes individually.
• Project context is often collected automatically through the interface.
• File operations and command execution are relatively visible.
• They work well for small refactors, function completion, and localized fixes.

However, graphical interaction can introduce overhead. An agent may display a confirmation prompt after every step, requiring the user to repeatedly shift attention. When a task involves many files, repeated testing, or complex debugging, the interaction cost can become significant.

2.2 Open Agent Frameworks: Fast and Flexible

Open agent frameworks generally provide more flexible tool-calling mechanisms. They can allow a model to quickly read files, search code, execute commands, and generate modifications. Because their workflows often impose fewer restrictions, they may have lower waiting time.

Their main advantages include:

• Shorter tool-call chains.
• Fewer default interaction steps.
• Good fit for rapid investigation and small code changes.
• The ability to adjust execution order dynamically based on the task.
• Easier integration with custom scripts and internal tools.

However, speed does not automatically mean higher reliability. If an agent lacks sufficient validation stages, it may:

• Fail to read the full project context.
• Modify code without running tests.
• Infer root causes from only a local error.
• Stop prematurely on complex tasks.
• Produce concise conclusions while missing important risks.

Open frameworks are well suited to scenarios where a useful result is needed quickly. For changes to critical systems, however, teams should still add testing, review, and rollback procedures.

2.3 Terminal-Based Research Agents: Focused on Retrieval, Analysis, and Knowledge Synthesis

Research-oriented terminal agents are generally strong at breaking down questions, searching for information, comparing alternatives, and writing detailed conclusions. They function more like technical research assistants with tool access than code editors.

They are useful for:

• Investigating how to use a technology.
• Comparing architectural approaches.
• Analyzing error messages and their surrounding context.
• Summarizing information from multiple sources.
• Writing design documents, research reports, and implementation recommendations.

Their main limitation is that research often requires many rounds of work. An agent may first create a retrieval plan, then visit multiple sources, organize the collected information, and cross-check claims. Without stable web access or retrieval capabilities, its practical performance can be significantly constrained.

Research agents may also generate lengthy intermediate reasoning and analysis, so their response time is not always ideal. They are appropriate for tasks that prioritize information completeness and depth of argument, but not necessarily for requests such as “change one line of code immediately.”

2.4 Terminal-Based Coding Agents: Strong Execution, Potentially Higher Approval Costs

Terminal-based coding agents usually have stronger project-operation capabilities. They can traverse directories, edit files, run tests, inspect logs, and repeat fixes. Their focus is on completing the full loop from a problem description to a verifiable result.

A typical workflow may include:

1. Understanding the task objective.
2. Inspecting the project directory and configuration.
3. Locating relevant code.
4. Creating a modification plan.
5. Editing one or more files.
6. Running builds or tests.
7. Adjusting the implementation based on errors.
8. Summarizing changes and validation results.

This process can produce more complete outcomes, but it also takes longer. In particular, when security approvals are enabled by default, file writes, command execution, network access, and batch changes may all require user confirmation. If a task triggers more than twenty approval requests, total completion time can increase significantly even if the model itself responds quickly.

Therefore, a terminal coding agent that appears “slow” is not necessarily weaker at reasoning. It may simply be performing more real operations and safety checks.

3. Why Response Speed Can Vary So Much

The total time required by an agent can be approximated as:

Total time = model generation time + tool execution time + network wait time + user approval time + retry time

The differences between agents usually come from the following factors.

3.1 Different Numbers of Tool Calls

Some agents generate an answer directly. Others read files, search code, and run tests before responding. More tool calls often lead to more complete results, but they also increase total time.

3.2 Different Context Sizes

If an agent sends large amounts of project content with every request, model inference may become slower. By contrast, sending only local files and essential error information can improve speed, but may reduce the accuracy of the agent’s judgment.

3.3 Whether Real Validation Is Performed

Agents that only generate code are usually fast. Agents that actually run tests, build projects, and inspect logs will inevitably require more time. These two approaches should not be evaluated only in terms of speed.

3.4 Different Approval Mechanisms

Automatic execution reduces waiting but can increase the risk of unintended operations. Step-by-step approval is safer but significantly increases interaction cost. Enterprise environments typically need tiered configurations that balance security and efficiency.

3.5 Different Network Tool Configurations

Web queries, documentation retrieval, and remote resource access depend on tool configuration, network conditions, and permissions. If reliable retrieval is unavailable, an agent’s ability to analyze external information will be limited. This should not be attributed solely to model quality.

4. Why Longer Answers Are Not Always Better

Some agents proactively generate report files, save operation logs, or provide detailed summaries even when the user did not explicitly request them. This behavior may reflect stronger awareness of task completion, but it can also create unnecessary files and additional review work.

When judging result quality, focus on whether the agent:

• Correctly understood the task objective.
• Modified the right files.
• Explained why the changes were made.
• Ran necessary tests.
• Reported unresolved issues.
• Made the operations reproducible.
• Avoided unrelated files and meaningless output.

A long response without validation results may be less valuable than a short patch that can be applied directly. Conversely, a complex task answered with only a few sentences may indicate that the agent did not perform sufficient investigation.

5. Establish a Consistent Evaluation Method

To compare different agents fairly, use the same set of tasks and record the following metrics.

Task Completion

Did the agent actually complete the requested work, rather than merely offering suggestions? Tasks can include code generation, bug fixing, project analysis, research, and documentation writing.

First-Pass Success Rate

Did the agent pass tests or reach the expected result on its first attempt? This metric often reflects practical efficiency better than response speed alone.

Tool-Call Efficiency

Track the number of files read, commands executed, repeated calls, and ineffective operations. A high number of calls is not always negative, but excessive repetition usually indicates weak planning.

Number of Human Interventions

Count approvals, corrections, and follow-up explanations. In enterprise workflows, the cost of human intervention is often more important than the cost of model calls.

Result Reproducibility

Given the same input and project state, can the agent produce comparable results? Reproducibility is important for team collaboration, automated pipelines, and issue tracking.

Security

Pay close attention to whether the agent deletes files, overwrites configurations, runs high-risk commands, or accesses unnecessary external resources without confirmation. Speed must be built on controllable permissions.

6. Selection Guidance for Different Scenarios

If the goal is to complete a localized code change quickly, prioritize an agent with a short tool chain and simple interaction flow.

If the task requires frequent diff inspection and file-by-file review, a graphical coding agent is more suitable.

If the work involves technical research, architecture comparison, and information synthesis, choose a research-oriented agent with strong retrieval capabilities and support for long analytical workflows.

If the task requires multi-file changes, test execution, and iterative debugging, choose a coding agent with comprehensive terminal capabilities and configure its approval strategy carefully.

For production environments, consider a tiered permission model:

• Allow read operations by default.
• Allow standard test commands to run automatically.
• Require batch confirmation for file writes.
• Require separate approval for deletion, deployment, and external network access.
• Preserve diffs for all changes and support rollback.

Conclusion

When agents use the same LLM, their differences fundamentally come from system design outside the model. The number and type of tools determine what an agent can do. Orchestration determines how it does it. Permission mechanisms determine whether it can complete a workflow continuously. Output strategies determine whether results are easy to review and reuse.

For this reason, an agent should not be judged solely by its response time or answer length in a single task. A more meaningful evaluation considers task completion, validation capability, human intervention, tool-call efficiency, security, and reproducibility.

For developers, the best agent is not necessarily the one with the most features. It is the one that best matches the task’s complexity, risk level, and team workflow.