Cursor Debug Mode Review: What You Need to Know Before You Dive In

TL;DR

1. The Mechanism: Cursor Debug Mode works by automatically injecting logging statements to capture variable values during bug reproduction.
2. The Good: It performed well in my test scenario and successfully fixed the logic error.
3. The Bad: It requires manual bug reproduction, involves multiple rounds of "log-and-restart," and demands heavy human intervention.
4. The Future: While logging is a solid first step, the next evolution will likely shift towards "Runtime Snapshots" to eliminate the need for manual reproduction and solve more complex bugs.

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Debugging is definitely one of the biggest pain points when working with coding agents. Cursor recently released Debug Mode, attempting to solve this by automatically injecting logs, reproducing the bug to capture context, and then applying a fix.

Here’s how it works:

1. Describe the bug: Select Debug Mode and describe the issue. The agent generates hypotheses and adds logging.
2. Reproduce the bug: Trigger the bug while the agent collects runtime data (variable states, execution paths, timing).
3. Verify the fix: Test the proposed fix. If it works, the agent removes instrumentation. If not, it refines and tries again.

We couldn't wait to test it out. While Cursor released an impressive demo video, there are some realities of engineering that they don't tell you. In this post, we’ll look at the clever design details of Debug Mode, as well as the pitfalls you need to watch out for.

The Test Scenario: Missing Discount

To recreate a realistic environment, we set up a typical Java backend project.

• Tech Stack: Java + H2 Database
• Business Logic: The database stores "User" and "Product" information. Administrators can set a discount when entering product details.
• The Bug: When a user purchases a discounted item, the final settlement amount remains at the original price. The discount is failing to apply.
• Our Goal: Find the root cause using Cursor Debug Mode.

Step 1: Identifying the Bug

First, let's look at the bug.

The system should apply the coupon correctly, but instead, it returns "Invalid Coupon."

Step 2: Agent Analysis & Instrumentation

Let's turn on Debug Mode in Cursor and describe the problem to the agent.

Cursor starts analyzing the code files, proposes several hypotheses about what might be wrong first.

Then it adds logging statements to the code.

Observation: The User Experience here is actually quite good. The logs are collapsed by default and include clear comments, which likely helps the AI clean them up later.

Step 3: The Friction Point (Manual Restart)

Now, Cursor gives us instructions: restart the application and reproduce the bug.

This is a point worth discussing. Fortunately, the bug in this demo is easy to reproduce. However, in real-world scenarios, many bugs are hard to trigger, and those are exactly the bugs Debug Mode currently cannot solve.

Additionally, it would be much better if Cursor could automatically build and restart the application for me. This is a capability many other coding agents have already implemented. Since this is a Java project, the restart process isn't instant—I have to manually stop, build, and run.

Step 4: Capturing Data & Finding the Cause

All right, I’ve manually rebuilt the app and triggered the bug.

Cursor automatically generates a debug.log file in the workspace's .cursor directory containing the output. Let’s look at the data structure:

{
    "id": "log_1765444581954_extract",
    "timestamp": 1765444581954,
    "location": "DemoApplication.java:70",
    "message": "Extracted values",
    "data": {
        "dbStatus": "ACTIVE",
        "dbStatusIsNull": false,
        "dbCategory": "FOOD​",
        "dbCategoryIsNull": false,
        "minAmount": "50.0",
        "minAmountIsNull": false,
        "expiryDate": "2025-12-31",
        "expiryDateIsNull": false,
        "categoryInput": "FOOD"
    },
    "sessionId": "debug-session",
    "runId": "run1",
    "hypothesisId": "A,B,C"
}

• timestamp: Time of the log.
• location: Line of code.
• hypothesisId: Which hypothesis this data validates.
• data: The specific runtime values captured.

Cursor reads this log content in real-time.

I click "Proceed" to let Cursor use this data to start fixing the bug.

It validates the hypotheses one by one and... it found the issue! It turns out the category value "FOOD" stored in the database contained an invisible whitespace character!

Step 5: Verification and "Flow"

Cursor modifies the code and successfully fixes the issue. Next, it asks me to reproduce the problem again to check if the fix worked.

At this point, human intervention is required again. This breaks the "flow state". I have to stop what I'm doing, rebuild the program, wait for it to launch, and manually test the UI.

I verified it, and thankfully, the problem was fixed.

The "Mark Fixed" Button

The story doesn't end there. Cursor continues to read new logs to verify if the bug persists. In this validation phase, Cursor relies on a "Human-in-the-Loop" design.

Why? Because AI doesn't genuinely "know" if a problem is fixed unless you describe the expected outcome with extreme precision. Some bugs might look fixed but introduce regressions. So, there is a "Mark Fixed" button. The AI only truly stops when you confirm the fix.

What we know about the Debug Mode

Cursor's Debug Mode essentially standardizes a workflow that many developers were already doing manually with Chat mode. It effectively stabilizes the agent loop and, to its credit, it successfully found the bug.

However, there are significant limitations:

1. The "Must Reproduce" Barrier

Cursor's premise is that you must be able to trigger the bug right now.
Real-world bugs are often:

• Flaky: It happens 1 time out of 10. Do you want to restart and run the test 10 times with the AI waiting?
• Environment Dependent: The bug might only happen with specific production data that you don't have locally. If you can't reproduce it locally, Cursor is flying blind. It has to resort to guessing.

2. The Expensive "Trial & Error" Loop

The process of "Inject Logs -> Restart Service -> Manually Click/Trigger -> Analyze Logs" is extremely slow.
If the AI guesses the wrong location for the logs (which is common), this entire loop has to be repeated. In compiled languages like Java or C++, your time and patience are drained by these constant restarts. Plus, it burns through your Fast Quota rapidly.

3. Heavy Human-in-the-Loop

Despite Cursor emphasizing that "human-in-the-loop verification is critical", the reality is that the current implementation feels heavy. I have to build, restart, verify, and click "Proceed" constantly. I would prefer an autonomous agent that handles the build/verify cycle, only asking me to "Mark Fixed" at the very end.

4. Code Pollution

Cursor retrieves information by modifying your source code (inserting logs). Although it tries to remove this instrumentation after you click "Mark Fixed," there is always a risk of accidentally committing this "garbage code" to your repository if the agent crashes or you lose track of the changes.

The Future of AI Debugging: Beyond "Print Statements"

Cursor's Debug Mode is a significant milestone—it proves that AI can autonomously navigate the debugging loop. However, technically speaking, it is automating a traditional, manual method: printf debugging.

While logging is a solid first step, the next evolution will likely shift towards Runtime Snapshots to eliminate the need for manual reproduction and solve more complex bugs.

Why? Because in a cloud-native, microservices, or complex state-management world, the cost of "Edit -> Compile -> Restart -> Reproduce" is simply too high. The ideal debugger should be an observer, not an intruder.

Syncause: The Snapshot Approach

This philosophy of Deep Instrumentation (Runtime Snapshots) is exactly what we are building at Syncause.

Instead of asking the AI to guess where to put logs and waiting for a restart, Syncause silently records the execution context in the background. It decouples "data collection" from "bug reproduction".

The Syncause Workflow:

1. Bug Happens? (Even if it was 5 minutes ago, or happened in a flaky scenario).
2. Just Ask the AI: "Why is the cart total wrong?" No log injection. No restarts. No manual reproduction.
3. Instant Answer: Because we capture the memory snapshot (stack traces, variable values, return states) at the moment of execution, the AI can inspect the "crime scene" immediately without needing to recreate it.

Here is the comparison:

Cursor Debug Mode:

The bug happens → Inject logs → Rebuild & restart → Reproduce again → Rebuild & restart → Validate

Syncause AI Debugger:

The bug happens → Ask the AI → Runtime snapshot → Fix instantly → Validate

Cursor's Debug Mode is a fantastic tool for quick scripts and straightforward logic. But if you want to solve the latency and friction issues inherent in the "log-and-restart" loop, you need a runtime inspector.

Best of all, the Syncause AI Debugger isn't locked to a specific IDE—it works as an extension for VS Code, Windsurf, Antigravity, and more.

If this is your debugging pain as well, you might want to give Syncause a try.