How to Help AI Debug Your Code Better

Coding agents are getting better at writing code. But when it comes to debugging real-world bugs, most AI still struggles — not because the models are weak, but because they lack runtime context.

This post breaks down why AI debugging fails in practice, what Cursor’s Debug Mode gets right, where prompt-based debugging falls apart, and how capturing runtime context directly inside your editor fundamentally changes how AI fixes bugs.

Why AI Is Bad at Debugging: Without Runtime Context

Most AI debugging today looks like this:

• You paste some code
• You describe the bug
• The model scans the codebase
• It makes an educated guess

This works for syntactic errors and obvious logic bugs. It fails badly for:

• State-related issues
• Timing problems
• Environment-specific behavior
• Non-deterministic bugs

The reason is simple: AI does not see what actually happened at runtime.

When humans debug, we rely on:

• Function parameters at execution time
• Call stacks
• Logs
• Traces
• Intermediate variable values

Without this data, AI is forced to hallucinate the most “reasonable” explanation — which is often wrong. Debugging is not a code-reading problem. It is a runtime observation problem.

Cursor Debug Mode: A Structured Workflow That Actually Works

Cursor introduced a Debug Mode that many developers immediately found effective.

At its core, Debug Mode enforces a standard debugging workflow:

1. Add logs
2. Restart the application
3. Reproduce the bug
4. Inspect the output
5. Fix the issue

This works because it forces AI to stop guessing and start testing hypotheses against real data.

The important insight here is not Cursor itself. It’s the workflow:

Hypothesize → Instrument → Observe → Verify → Fix

When AI is guided through this loop, its accuracy improves significantly.

Method 1: Debugging with Prompt Engineering

If you are not using Cursor, you can partially replicate this workflow using prompt engineering.

Act as a Debugging Agent and follow this strict workflow to resolve the bug: "{bug}"

1. **Analyze & Hypothesize**: 
   - Review the codebase.
   - List **multiple distinct hypotheses** (e.g., H1, H2, H3) for the root cause.
   - Explain the reasoning for each.

2. **Instrument**: 
   - Insert logging statements to capture specific data needed to prove or disprove your hypotheses.
   - Write the logs to a `debug.json` file.
   - Append a specific trailing comment tag (e.g., `print(x) // DEBUG_TAG`) to every inserted line. This ensures the code can be easily identified for removal.

3. **Execute & Diagnose**: 
   - Run the reproduction steps and read the `debug.json` output.
   - **Output a Verification Report**: For EACH hypothesis (H1, H2...), explicitly state its status (**CONFIRMED** or **REJECTED**) citing evidence from the logs.
   - If all are rejected, return to Step 1 with new insights.

4. **Fix**: Implement the code correction based on the **CONFIRMED** hypothesis.

5. **Verify**: Re-run the reproduction steps to confirm the bug is fixed.

6. **Cleanup**: ONLY after verification is successful, remove all lines containing the `// DEBUG_TAG` and delete the `debug.json` file.

Start from Step 1 now.

With strong models, this approach can work surprisingly well.

However, it has serious limitations:

1. Unstable Code Instrumentation: The AI must modify your code to observe behavior. This often introduces noise, formatting issues, or even new bugs.
2. Still Requires Bug Reproduction: If the bug cannot be reliably reproduced, the workflow collapses.
3. Poor Bug Localization: Without execution data, the AI may instrument the wrong area entirely.
4. Token Inefficiency: You pay tokens for code changes whose only purpose is observation.

Prompt engineering improves discipline, but it does not change the fundamental problem: AI is still blind until you run the code again.

Why Logs Are a Hack, Not a Solution

Logs are a workaround for missing observability.

They exist because:

• We cannot see execution state directly
• We need to print values to understand behavior

For AI, logs are even worse:

• They pollute the codebase
• They must be planned in advance
• They capture only what you guessed might matter

A human debugger with a breakpoint does not add logs everywhere. They inspect state at the moment of failure. If AI is going to debug like a human, it needs the same thing: runtime context without code modification.

Method 2: Capture Runtime Context Automatically

Now consider a different approach: What if, when a bug happens, the AI already has access to:

• Function arguments
• Local variables
• Stack traces
• Execution flow
• Relevant logs

No code changes. No restarts. No reproduction.

This is what a debugger does for humans — and what an IDE extension can do for AI.

Tools like Syncause integrate an SDK into your application and surface runtime data directly in your editor, inline with the relevant code.

From the AI’s perspective, the workflow changes completely:

• No need to analyze the entire codebase blindly
• No need to insert logs
• No need to guess where the bug might be

The AI starts with ground truth.

How This Changes AI Debugging in Practice

With runtime context available upfront:

• Bugs can be fixed in one pass
• Non-reproducible issues become debuggable
• AI suggestions become precise instead of probabilistic
• Token usage drops because observation no longer requires code edits

Most importantly, debugging stops being a loop:

add logs → restart → reproduce → retry

And becomes a direct action:

observe → reason → fix

This is the same shift that made traditional debuggers indispensable — now applied to AI-driven development.

Prompt Engineering vs Runtime Context

Approach	Requires Reproduction	Modifies Code	Debug Accuracy
Prompt-only Debugging	Yes	Yes	Medium
Cursor Debug Mode	Yes	No	High
Runtime Context Extension	No	No	Highest

Prompt engineering improves discipline.

Debug modes improve workflow.

Runtime context changes the game.

Final Thought

AI does not fail at debugging because it lacks intelligence.

It fails because it lacks visibility.

If you want your coding agents to debug like engineers instead of guessing like reviewers, give them access to what actually happened — not just what the code looks like.

That is the difference between reading code and understanding behavior.