TL;DR: Removing the requirement to change a file name fixed the agents inability to complete the code change request.
What to expect in this report:
The Issue: The agent is unable to edit the file and outputs nothing after being prompted to make a change to the code.
The Contents: The issue from the point of the agents failure through resolution is documented, including prompts to the agent (subsequent to the failure) to identify the root cause of the issue. Followed by the resolution which enabled the agent to complete the original task/request.
—(Info)—
Platform: Windows
Cursor Version: 0.49.5
Mode: Agent
Model: gemini-2.5-pro-exp-03-25
—(Issue)—
Observed Issue: Steps leading up to the edit of the file seemed to go per usual, before hitting a ~10 second “generating…” process, and then outputting no result with no code changes.
Follow up prompt: “No changes were made, can you analyze why this happened?”
Agent output: “My apologies, I described the plan in detail but failed to actually generate the code edit command in my previous response.”
---(Requesting Bug Report from Agent)---
In order to assist with identifying the root cause I asked the agent to output insights into the process to generate a bug report:
---(Begin Agent Output)---
Bug Report Information:
- Goal: The user requested a significant refactoring of the [redacted] file [redacted]. The goal was to:
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Rename the class to [redacted].
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Change the data storage strategy from parsing all data upfront [redacted] to storing only [redacted] names [redacted] and a map of names to raw JSON objects [redacted].
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Implement a detailed, multi-stage category selection workflow including:
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Editable properties [redacted].
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State variables [redacted x3].
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Specific public functions [redacted x9].
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Private helper functions [redacted x2].
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Modified [redacted] logic for initial JSON parsing and map population.
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Remove old test functions and variables.
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Expected Assistant Action: After confirming the detailed plan, the assistant was expected to generate and propose an edit_file tool call targeting [redacted] with the necessary code changes to implement the refactoring.
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Actual Outcome: The assistant acknowledged the plan and stated it would apply the changes but did not generate the edit_file tool call. No changes were made to the target file.
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Context Provided:
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Target File: [redacted] (Full content was attached in previous turns).
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User Instructions: Detailed, step-by-step requirements for the new category selection logic were provided (Seeding, Nomination, Fill/Cull phases).
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Cursor Rules: [redacted] coding rules were available.
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Workspace: [redacted]
- Potential Failure Reasons (Speculation):
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Complexity/Size of Edit: The requested refactoring was substantial, involving renaming, adding multiple fields and functions, and significantly altering existing logic [redacted]. This might have exceeded the complexity or size limits for a single edit_file operation that the model could reliably generate.
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Internal Generation Error: The model might have encountered an error while constructing the code_edit block or the tool call itself, potentially due to the intricate logic or specific [redacted] syntax required.
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State Mismanagement: The model might have lost track of the exact goal or the planned action between confirming the plan and generating the tool call.
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Tool Constraints: There might be underlying constraints on the edit_file tool regarding the length or complexity of [redacted] code edits it can handle effectively.
---(End Agent Output)---
---(Requesting Context Window Analysis)---
In order to ensure there wasn’t any additional information being included in the context window that shouldn’t be there, I requested analysis from the Agent:
Prompt: “Can you analyze the context window and see if there is anything there being included that should not be?”
---(Begin Agent Response)---
Okay, let’s analyze the context being provided:
- User Info:
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OS: win32 10.0.19045
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Workspace Path: [redacted]
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Shell: C:\WINDOWS\System32\WindowsPowerShell\v1.0\powershell.exe
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Analysis: This provides general environment details. The workspace path is relevant for file operations. The OS and shell are less directly relevant to the [redacted] coding task itself but are standard pieces of information. They are unlikely to be incorrectly included or causing the issue.
- Cursor Rules Context:
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Contains 9 detailed rules for [redacted] coding [redacted].
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Analysis: These rules are highly relevant as the task involves writing [redacted] code. They provide specific guidance on syntax, patterns, and potential pitfalls. This context is appropriate and helpful.
- Additional Data:
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<current_file>:
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Path: [redacted]
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Line: 33
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Content: [redacted]
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Analysis: This correctly identifies the file the user is focused on. The line number and content point to a specific location within the old code structure that is intended to be refactored. While the specific line will change/be removed, knowing the target file is crucial. This context is relevant.
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<attached_files>:
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Contains the full content of [redacted]
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Analysis: This is essential context. Modifying or refactoring a file requires knowing its current contents. Providing the full file is necessary for the requested large-scale changes.
Conclusion:
Based on the information presented, there doesn’t appear to be any context included that should not be there.
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The core file content [redacted] is necessary for the refactoring task.
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The [redacted] coding rules are relevant guidance for the language being used.
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The user/workspace information provides necessary environmental details, particularly the file path context.
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The current file/cursor indicates the user’s focus.
The issue encountered previously (failing to generate the edit) is less likely due to irrelevant information being included and more likely related to:
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Complexity/Size of the Request: The refactoring itself is significant, involving substantial changes to structure, logic, and adding many new elements. Generating a correct and complete edit for such a large change might be challenging.
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Internal Model/Tool Limitations: There might be internal constraints within the model or the edit_file tool regarding the scale or specifics of the [redacted] code manipulation required.
Therefore, the context provided seems appropriate and necessary for the task at hand. The problem likely lies elsewhere in the process of generating or applying the complex requested changes.
---(End Agent Output)---
---(Requesting Request Complexity Analysis)---
Prompt: “Can you analyze and provide any data driven insights to the complexity of the edit_file request that would give more insight?”
---(Begin Agent Response)---
Okay, let’s break down the complexity of the requested edit_file operation with some data-driven points based on the proposed changes and the original file:
- Scale of Change (Lines Affected):
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Original File Size: Approximately 310 lines (including comments and blank lines).
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Estimated Lines to Add: Roughly 100-150 lines for new properties, state variables, function definitions [redacted x9], and the revamped [redacted] logic.
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Estimated Lines to Delete: Roughly 50-70 lines, including the old [redacted] functions (and commented-out ones), [redacted], and the [redacted] variable.
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Estimated Lines to Modify: Significant modification/replacement of the [redacted] body (~2 lines replaced by ~20-30 lines), changing visibility specifiers on existing helper functions (~10-15 lines touched), and the class definition line (1 line).
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Net Change: A substantial increase in line count, but more importantly, a large percentage of the original file’s logic is being either removed or replaced.
- Structural Transformation:
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Data Structure: The primary data storage shifts from a single, fully-parsed array [redacted] to two distinct structures: a simple list of identifiers [redacted] and a map holding raw JSON data [redacted]. This requires rewriting how data is loaded and accessed.
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Control Flow: The main purpose changes from executing test cases to managing a complex, multi-stage state machine for category selection [redacted]. This involves adding significant state management logic.
- Functional Granularity:
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Functions Added: Approximately 10-12 new functions are introduced, each encapsulating a specific part of the new workflow.
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Functions Removed/Replaced: Several existing functions are removed, and the core [redacted] function is completely rewritten.
- Algorithmic Complexity Introduced:
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JSON Parsing & Mapping [redacted]. The new [redacted] needs to parse the JSON, iterate through potentially many categories, extract specific fields, handle potential errors during extraction, and populate both the [redacted] list and the [redacted]. This is more complex than the previous direct parsing into [redacted].
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Selection Logic: This function involves consolidating nominations (handling duplicates), checking list lengths against [redacted], conditionally filling the list with random selections from a different pool, and signaling whether a separate [redacted] is needed. This involves non-trivial list manipulation and conditional branching.
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Randomization: Requires integrating with [redacted] and implementing shuffling and random selection logic.
- Dependency Changes:
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Adds a new dependency on [redacted]
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Implicitly relies more heavily on map (map(string, [string]value)) operations.
Summary of Complexity Insights:
The requested edit represents a major refactoring, not just an incremental change. Key complexity drivers include:
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High Volume of Changes: A large number of lines are added, deleted, and modified relative to the original file size.
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Fundamental Structural Shift: Core data structures and the device’s primary control flow are being completely redesigned.
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Introduction of Complex Algorithms: The new [redacted]parsing/mapping and the [redacted] logic are algorithmically more complex than the original code.
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Increased State Management: The device now needs to manage the state across multiple variables [redacted] through a defined process.
This level of transformation, involving simultaneous changes to data structures, control flow, function definitions, and algorithms across a significant portion of the file, likely pushes the boundaries of what can be reliably handled by a single, automatically generated edit_file operation, contributing to the previous failure.
---(End Agent Output)---
---(Iterative Step-By-Step Troubleshooting)---
Prompt:
“Let’s try iteratively removing portions of the request to see if it can resolve the edit_file request.
I have now done step 1 myself renaming the [redacted] file myself as well as renaming the class inside.
Lets remove that from the request and proceed from the 2nd step and see if that fixes it. If not we will continue to reduce complexity until we identify the root cause.”
Result:
The agent was able to successfully make the changes to the file.
Image showing the changes being successfully made within the file
---(Conclusion)---
Conclusion:
The first step here which namely removed the requirement for the Agent to rename the file it was working in resulting in the Agent being able to complete the request leads me to believe it was not the complexity of the request, but the inability for the agent to rename the file it was working in.
---(Reproducibility)---
Reproducing the Bug:
In theory, you would include a request that requires the Agent to rename the file its currently working in.
(Perhaps this could be a larger issue with permissions?)
