Debugging with AI: How Claude Code, Copilot, and Cursor Find Bugs

You've been staring at a stack trace for 40 minutes. The error points to line 247, but the actual bug is three files away in a race condition you wrote six months ago. You know the drill -- add print statements, set breakpoints, re-run, squint, repeat. Debugging has always been the most time-consuming part of programming, and traditional tools haven't changed much since the 1990s.

That changed in 2026. AI debugging tools went from "explain this error message" party tricks to genuine bug-finding engines that trace through your codebase, reason about data flow, and pinpoint root causes that would take you hours to find manually. AI-assisted debugging improved problem-solving rates from 4.4% in 2023 to over 69% in 2025, and the tools have only gotten sharper since.

This guide covers how to actually debug with the three major AI tools -- Claude Code, GitHub Copilot, and Cursor -- with real prompts, real workflows, and honest takes on what each tool is good (and bad) at finding.

📋 What You'll Need

• At least one AI coding tool installed -- Claude Code (terminal), GitHub Copilot (VS Code/JetBrains), or Cursor IDE
• A codebase with bugs -- you definitely have one of those
• Comfort with the terminal -- especially for Claude Code workflows
• A willingness to share code context with AI -- these tools need to read your code to debug it
• 15-30 minutes -- enough time to try each workflow on a real bug

🧠 How AI Debugging Actually Works (It's Not Magic)

Before diving into tool-specific workflows, it helps to understand what these tools are doing under the hood. AI debuggers don't execute your code. They don't set breakpoints. They don't have access to your runtime state (usually). What they do is reason about code statically -- reading your source, understanding data flow, and predicting where things break.

The process looks roughly like this:

┌──────────────┐     ┌───────────────────┐     ┌──────────────────┐
│  You provide │────►│  AI reads code +  │────►│  AI returns root │
│  error + code│     │  reasons about    │     │  cause + fix     │
│  context     │     │  control flow     │     │  suggestion      │
└──────────────┘     └───────────────────┘     └──────────────────┘

The quality of the output depends entirely on how much context you provide. Feed the AI just an error message, and you'll get a generic Stack Overflow answer. Feed it the error message, the relevant code, the test that fails, and a description of expected behavior -- and you'll get something genuinely useful.

What AI Debuggers Are Good At

The takeaway: AI debuggers excel at bugs that are visible in the source code. They struggle with bugs that only manifest at runtime under specific conditions.

🧪 Claude Code: The Deep Reasoning Debugger

Claude Code runs in your terminal and operates as an autonomous agent. For debugging, this means it can read multiple files, trace through your codebase, run commands, execute tests, and iterate -- all without you manually opening files or copying code into a chat window.

When to Reach for Claude Code

Claude Code shines when the bug isn't obvious from a single file. If the error is in views.py but the root cause is a misconfigured serializer three imports deep, Claude Code will trace the full chain. It's also excellent for debugging legacy code you didn't write, because it reads the entire context before answering.

The Debugging Workflow

Step 1: Feed it the error with context.

Open your terminal and start Claude Code in your project directory:

cd /path/to/your/project
claude

Then describe the problem. Be specific:

The test test_create_order in tests/test_orders.py fails with
"AttributeError: 'NoneType' object has no attribute 'id'" on line 45.
The test was passing yesterday. I changed the UserSerializer to
add a new field. Find the root cause and fix it.

Step 2: Let it explore.

Claude Code will read the test file, the view it tests, the serializer you mentioned, and any related models. You'll see it working through the file tree in your terminal. Don't interrupt -- let it finish its analysis.

Step 3: Review the diagnosis.

A typical Claude Code debugging response identifies:
- The exact line where the bug manifests
- The root cause (often in a different file)
- Why the change you made triggered it
- A fix with the specific code changes needed

Practical Debugging Prompts for Claude Code

Here are prompts that consistently produce useful results:

# For a failing test
"Run pytest tests/test_orders.py::test_create_order and debug
the failure. Read the source code it touches and identify the
root cause."

# For a production error
"Here's a traceback from production: [paste traceback].
Trace through the codebase to find what causes this and
suggest a fix. Check if there are similar patterns elsewhere
that might have the same bug."

# For a regression
"This endpoint /api/users/ returned 200 yesterday but returns
500 today. Check the git diff from the last 3 commits and
identify which change broke it."

# For performance issues
"The function process_batch in utils/batch.py takes 45 seconds
for 1000 items. Profile the logic, identify the bottleneck,
and suggest an optimization."

Claude Code's Secret Weapon: Git-Aware Debugging

Because Claude Code runs in your terminal, it has access to your git history. This makes regression debugging extremely effective:

# Ask Claude Code to diff and find the breaking change
"Run git log --oneline -10, then check the diffs of recent
commits. The user login flow broke sometime this week.
Find which commit introduced the bug."

This is something neither Copilot nor Cursor can do natively -- trace through actual git history to bisect a regression.

🐙 GitHub Copilot: The Inline Debugging Assistant

Copilot takes the opposite approach from Claude Code. Instead of an autonomous agent in the terminal, Copilot lives inside your editor and assists while you're actively working on the code. Its debugging strengths are in breakpoint-assisted analysis, inline error explanation, and quick fixes for common patterns.

When to Reach for Copilot

Use Copilot when you're already looking at the buggy code in your editor and you need a second pair of eyes. It's fastest for single-file bugs, type errors, and issues where the fix is within arm's reach of the error.

The Debugging Workflow

Method 1: The copilot-debug Terminal Command

This is Copilot's most underrated debugging feature. In VS Code's integrated terminal:

copilot-debug python manage.py test tests/test_orders.py

Copilot automatically configures a debugging session, sets relevant breakpoints, and launches the debugger. No more manually writing launch.json configs.

Method 2: Debug with Copilot (Agent Mode)

In Visual Studio 2026, you can click "Debug with Copilot" which:
1. Analyzes the failing test and related code
2. Reads recent changes that might be relevant
3. Sets intelligent breakpoints based on the error
4. Explains what it finds at each breakpoint during execution

Method 3: Chat-Based Debugging

Open the Copilot Chat panel (Ctrl+Shift+I / Cmd+Shift+I) and use these patterns:

/fix Why does this function return None instead of a User object?

@workspace The test test_create_order fails with AttributeError.
What's the likely cause?

#file:serializers.py #file:views.py Why would changing the
UserSerializer break the order creation endpoint?

Copilot's Debugging Strengths

Runtime-aware analysis. When you're in a debugging session, Copilot can see your current call stack, variable values, and watch expressions. Ask it:

Why is `user_id` None at this breakpoint? Trace back through
the call stack to find where it should have been set.

This is genuinely useful -- it combines static code analysis with actual runtime state, which is something Claude Code and Cursor can't do.

Conditional breakpoint suggestions. Ask Copilot:

Suggest a conditional breakpoint to catch when order.total
becomes negative.

It'll suggest the exact condition expression and which line to place it on.

Copilot's Debugging Weaknesses

Copilot's context window is limited to files you have open. If the bug spans modules you haven't opened, you'll need to manually open those files to expand Copilot's view. It also doesn't autonomously explore your codebase -- you have to point it at the right files.

⚡ Cursor: The Codebase-Aware Bug Hunter

Cursor occupies the middle ground between Claude Code's deep autonomy and Copilot's inline assistance. It understands your full repository (via codebase indexing) and can make multi-file debugging suggestions, but it works inside an IDE so you see everything happening in real-time.

When to Reach for Cursor

Cursor is the best choice when you suspect the bug involves multiple files and you want to see the AI's reasoning play out in your editor. It's particularly strong at catching bugs that involve data shape mismatches across module boundaries.

The Debugging Workflow

Method 1: Composer Debugging

Open Composer (Cmd+I / Ctrl+I) and describe the bug. Composer has access to your full codebase index:

The API endpoint POST /api/orders/ returns a 400 error with
"user field is required" even though the user is authenticated.
The request includes a valid JWT token. Find why the user
isn't being extracted from the request.

Composer will trace through your URL routing, middleware, authentication backend, serializer, and view to find the disconnect. It shows you exactly which files it reads and what it found.

Method 2: BugBot (PR-Level Debugging)

Cursor's BugBot, released in mid-2025, is an automated bug finder that scans your code changes against main:

1. Push your branch as a pull request
2. BugBot analyzes the diff automatically
3. It adds comments to the PR pinpointing potential issues
4. Each comment includes the problem, explanation, and suggested fix

BugBot catches patterns like:
- Missing await on async calls
- Misnamed function references
- Incorrect imports after refactoring
- Data shape mismatches between APIs

Method 3: Inline Chat Debugging

Select the buggy code, press Cmd+K / Ctrl+K, and ask:

This function sometimes returns undefined instead of an empty
array. Under what input conditions does this happen?

Cursor will analyze the function's edge cases and tell you exactly which inputs trigger the unexpected return value.

Cursor vs. Claude Code for Multi-File Bugs

Here's where the practical difference shows up. Given the same multi-file bug:

For most debugging sessions, Cursor is faster to start and easier to review. For deep, cross-cutting bugs in large codebases, Claude Code's ability to run commands and trace through git history gives it an edge.

🔄 The AI Debugging Playbook: A Step-by-Step Process

Regardless of which tool you use, effective AI debugging follows the same pattern. Here's the workflow that consistently produces the best results:

Step 1: Reproduce and Capture

Before touching any AI tool, get the exact error output. Run the failing test, trigger the bug, and capture:

# Run the specific failing test and capture the full output
# pytest with verbose and traceback options
pytest tests/test_orders.py::test_create_order -v --tb=long

Copy the entire output -- not just the error line. The traceback, the test setup, and any warnings all provide signal.

Step 2: Provide Context, Not Just Errors

Bad prompt:

Fix this error: AttributeError: 'NoneType' object has no attribute 'id'

Good prompt:

The test test_create_order in tests/test_orders.py fails with
"AttributeError: 'NoneType' object has no attribute 'id'" on line 45
of views.py.

This started failing after I added a "phone" field to UserSerializer.
The test creates a user via the factory, then creates an order linked
to that user. The user object exists in the database (verified with
a print statement), but the view receives None for request.user.

Expected: order is created with the authenticated user.
Actual: AttributeError because request.user is None.

The second prompt gives the AI enough context to reason about the problem without exploring dead ends.

Step 3: Verify Before Applying

Never blindly apply an AI-suggested fix. Always:

# 1. Read the suggested fix and understand WHY it works
# 2. Run the failing test to confirm it passes
pytest tests/test_orders.py::test_create_order -v

# 3. Run the full test suite to check for regressions
pytest tests/ -v --tb=short

# 4. Manually test the happy path if it's a user-facing bug

Step 4: Ask for Similar Bugs

After fixing one bug, ask the AI to scan for the same pattern elsewhere:

You found that the bug was caused by the serializer not including
the user field in validated_data after I added a new field.
Scan the codebase for other serializers that might have the same
issue -- where adding a new field could silently exclude existing
fields from validated_data.

This is where AI debugging pays compound interest. One bug fix becomes a codebase-wide audit.

📊 Tool Comparison: Debugging Features Head-to-Head

The Practical Recommendation

• Quick single-file bugs: Use Copilot. It's right there in your editor, and for simple errors it's the fastest path to a fix.
• Multi-file bugs with clear symptoms: Use Cursor. Its codebase awareness and visual diff review make multi-file debugging intuitive.
• Deep, mysterious bugs in large codebases: Use Claude Code. Its ability to autonomously explore, run commands, and reason across thousands of lines is unmatched.
• Regressions from recent changes: Use Claude Code. Git-aware debugging and the ability to diff commits makes regression hunting significantly faster.

🔧 Troubleshooting Your AI Debugging Workflow

"The AI keeps suggesting the wrong fix."
You're probably not providing enough context. Include the error message, the relevant code, what you've already tried, and what the expected behavior is. AI tools are only as good as the input they receive.

"Claude Code is reading too many files and wasting time."
Narrow the scope. Instead of "find the bug," say "the bug is in the order creation flow -- check views/orders.py, serializers/orders.py, and the related test." Pointed instructions save tokens and time.

"Copilot doesn't understand my project structure."
Open the relevant files in your editor tabs before asking for help. Copilot's context is limited to open files. Use @workspace in chat to expand its view, and tag specific files with #file:path/to/file.py.

"Cursor's BugBot flags too many false positives."
Calibrate by dismissing false positives consistently. BugBot learns from which suggestions you accept and which you reject. Also, keep your PRs focused -- a 50-file PR will generate more noise than five 10-file PRs.

"The AI fix passes the test but breaks something else."
Always run the full test suite after applying a fix, not just the failing test. Add a follow-up prompt: "Check if this fix could cause regressions in related modules." Prevention is cheaper than a second debugging session.

🚀 What's Next

• Build your prompt library. Save debugging prompts that work well for your codebase. A good prompt template saves 5 minutes per debugging session.
• Combine tools. Use Copilot for quick inline fixes during your normal flow, and switch to Claude Code when you hit a bug that resists quick analysis.
• Set up BugBot on your repos. Cursor's PR-level bug scanning catches issues before they reach main. It's free prevention.
• Learn to provide better context. The single biggest improvement to AI debugging isn't a better tool -- it's a better prompt. Include error output, expected behavior, recent changes, and what you've already tried.
• Read our guide on Claude Code workflows for a complete walkthrough of terminal-based AI development beyond debugging.

For a broader comparison of these tools' full feature sets, check out AI Coding Agents Compared. For Copilot-specific deep dives, see the GitHub Copilot Agent Mode Guide. And if Cursor is your daily driver, don't miss Cursor IDE Tips and Tricks.