AI-Assisted Debugging in Cursor

Production is returning 500 errors on 15% of checkout requests. It works flawlessly in development. It started after last Tuesday’s deployment, but the diff looks harmless — just a small refactor of the payment processing logic. The error message is the supremely unhelpful “Transaction failed.” Your CEO is asking for updates every 30 minutes.

You could spend hours manually reading logs, adding console.log statements, and trying to reproduce the issue locally. Or you can use Cursor’s debugging capabilities to systematically narrow down the problem in minutes instead of hours. The key insight: debugging is a search problem, and AI is very good at searching.

What You’ll Walk Away With

• A systematic prompt for analyzing error logs and extracting patterns the human eye misses
• A Debug mode workflow that instruments code, captures runtime data, and identifies root causes
• A “hypothesis generation” prompt that produces ranked, testable theories from symptoms
• A load test generation prompt for reproducing intermittent failures locally
• A post-mortem template that documents the bug, the fix, and the prevention strategy

The Workflow

Step 1: Gather symptoms with Ask mode

Before investigating code, collect every observable fact about the failure. Use Ask mode so you are purely reading and analyzing, not accidentally changing anything in a production-adjacent codebase.

Tip

Copy-paste prompt — Symptom analysis:

I have a production bug with these symptoms:

• Error: “Transaction failed” on checkout endpoint (POST /api/checkout)
• Frequency: ~15% of requests, seemingly random
• Environment: production only, cannot reproduce in dev
• Timeline: started after deployment on Tuesday at 14:30 UTC
• Pattern: seems to correlate with traffic spikes but not exclusively
• Error logs show the failure happens in the payment processing step
• No stack trace — the error is caught and wrapped in a generic message

@src/checkout @src/payment @src/middleware

Based on the code and symptoms, what are the top 5 most likely root causes, ranked by probability? For each hypothesis, tell me:

1. What evidence supports it
2. What evidence would confirm or rule it out
3. What specific log line or metric I should look for

The model will likely identify patterns like connection pool exhaustion, race conditions, timeout misconfigurations, or a missing error handler on a specific code path. Each hypothesis is testable, which is the critical part — vague theories waste time.

Step 2: Switch to Debug mode for runtime investigation

Cursor has a dedicated Debug mode designed for exactly this kind of problem. Press Cmd+. or Shift+Tab to switch to Debug mode. Unlike Agent mode, Debug mode follows a structured investigation process:

1. It explores relevant files and generates hypotheses
2. It adds instrumentation (log statements) to your code that report to a local debug server
3. It asks you to reproduce the bug
4. It analyzes the collected runtime data
5. It proposes a targeted fix based on evidence, not guesswork

The checkout endpoint at POST /api/checkout is returning "Transaction failed"
for ~15% of requests under load. The error happens in the payment processing
step. I can reproduce it locally using a load testing tool (k6 or similar)
that sends 200 concurrent requests.

Find the root cause using instrumentation.

Debug mode will add log statements at key points — database connection acquisition, payment API calls, transaction boundaries — and ask you to trigger the bug. When it collects the runtime data, it can often pinpoint the exact issue within minutes.

Note

Debug mode works best when you can reproduce the bug locally. If you can only observe the bug in production, provide production logs as context instead and use Ask mode or Agent mode for analysis.

Step 3: Reproduce the failure with a targeted load test

If you cannot reproduce the bug manually, ask Agent mode to generate a load test that triggers the exact conditions.

Tip

Copy-paste prompt — Load test generation:

@src/checkout/route.ts @src/payment/processor.ts

Generate a k6 load test script that:

1. Simulates the exact checkout flow (create cart, add items, initiate checkout, process payment)
2. Ramps from 10 to 200 concurrent users over 60 seconds
3. Uses realistic payment data (varying amounts, different card types)
4. Tracks: response times, error rates by status code, the specific “Transaction failed” error message
5. Reports which requests failed and the exact response body
6. Runs against localhost:3000

The goal is to reproduce the ~15% failure rate we see in production. Save as tests/load/checkout-stress.js.

Run the test locally. If it reproduces the failure, you have a reliable way to verify your fix later.

Step 4: Analyze the evidence with Agent mode

Once you have logs, error traces, or Debug mode output, use Agent mode to analyze the data and identify the root cause.

@logs/debug-output.log @src/payment/processor.ts

Here are the debug logs from the load test. Analyze them and answer:

1. Which requests failed and what do they have in common?
2. What is the timing pattern? Do failures cluster at specific intervals?
3. Is there a resource that's being exhausted (connections, file handles, memory)?
4. Can you identify the exact line where the failure originates?
5. What is the root cause?

Show me the specific code that needs to change and explain why.

A common finding in this scenario: the payment processor acquires a database connection but fails to release it on certain error paths (a “connection leak”). Under load, the connection pool exhausts and subsequent requests fail with a timeout that gets wrapped in the generic “Transaction failed” message.

Step 5: Implement and verify the fix

Once the root cause is identified, use Agent mode to implement the fix, add a regression test, and improve error handling so this class of bug is easier to diagnose next time.

Tip

Copy-paste prompt — Fix implementation:

@src/payment/processor.ts

The root cause is a database connection leak: when payment validation fails on line 47, the function returns early without releasing the connection acquired on line 32. Under load, the pool exhausts and subsequent requests timeout.

Fix this by:

1. Moving the validation BEFORE the connection acquisition (no connection needed for input validation)
2. Wrapping the entire connection usage in a try/finally that guarantees release
3. Replacing the generic “Transaction failed” error with specific error types: ValidationError, PaymentGatewayError, ConnectionTimeoutError — each with a unique error code
4. Adding connection pool metrics logging (pool size, active connections, waiting requests) on every checkout request
5. Creating a regression test in tests/payment/connection-leak.test.ts that verifies the connection is released even when validation fails

Run the load test after the fix to verify the error rate drops to 0%.

Step 6: Write the post-mortem

This is the step most teams skip, and it is the most valuable long-term. Use Ask mode to draft a structured post-mortem from the debugging session.

Based on this debugging session, write a post-mortem document covering:

1. Summary: what broke, who was affected, how long it lasted
2. Timeline: when it started, when detected, when fixed
3. Root cause: the connection leak in payment processing
4. Fix: what changed and why
5. Detection gap: why our monitoring didn't catch this sooner
6. Prevention: what we will do to prevent similar bugs (connection pool alerting, code review checklist for resource cleanup)
7. Action items with owners and deadlines

Save to docs/postmortems/2026-02-checkout-connection-leak.md

Advanced Technique: Git Bisect with AI

When the bug started after a specific deployment and you have a good test, use @git context to narrow down the exact commit:

@git

The checkout load test passes on commit abc123 (Monday) but fails on HEAD (Tuesday).
There are 12 commits between them. Help me set up a git bisect:

1. Create a script that runs the load test and returns exit code 0 if fewer than 1% of
   requests fail, exit code 1 otherwise
2. Show me the git bisect commands to find the exact commit that introduced the regression

This narrows 12 commits down to 1 in about 4 iterations.

When This Breaks

Debug mode instrumentation changes the bug’s behavior. Adding log statements can alter timing enough that race conditions disappear (the “Heisenbug” problem). If this happens, use lighter instrumentation: increment atomic counters instead of logging strings, or use process.hrtime() timestamps that don’t require I/O.

The AI generates a plausible but wrong root cause. AI models are excellent at pattern matching but can be confidently wrong. Always verify the hypothesis before implementing a fix. The load test should confirm: if the root cause is correct, the fix should reduce the error rate to near zero.

Agent mode suggests a fix that papers over the symptom. A common bad fix: adding a generic retry wrapper around the failing operation instead of fixing the leak. Retries mask connection pool exhaustion temporarily but make it worse under sustained load. Push back on band-aid fixes — ask the AI to find the actual cause, not a workaround.

Cannot reproduce locally. Some bugs only manifest with production data volumes, network latency, or specific hardware. In these cases, instrument production with structured logging and analyze the logs with Ask mode. Use @web to search for known issues with the specific library or service that is failing.

The error is in a third-party library. If the bug trace leads into node_modules, use @docs to check the library’s issue tracker and changelogs. Ask: “Has this library been reported to have connection leaks or timeout issues in recent versions?”

What’s Next

API Integration Build resilient API integrations with retry logic and circuit breakers that prevent these kinds of failures.

Performance Optimization Proactively find bottlenecks before they become production incidents.

Debugging Workflows Deep dive into advanced debugging patterns and Cursor-specific techniques.