Connecting LLMs to my debugging flow to fix a memory crash

Source: Dev.to

Introduction

Every engineer has a “Mystery Case” story. For a long time, my service ran perfectly for weeks and then, at the most unexpected times, violently consumed memory and died. I tried logs, code optimizations, and even a “False Victory” fix that stopped the crash for a week before it returned. Manual investigation was impossible because the sheer volume of data hid the important facts.

Exporting Metrics & Pattern Matching

I exported the raw metric data and treated the AI as a pattern matcher.

Prompt

Analyze this dataset. Find the exact timestamps where memory allocation spikes > 20% in under 60 seconds.

Result
The AI identified two specific seconds in time where the spikes occurred.

Correlating Spikes with Logs

Using the timestamps, I asked the AI to generate a targeted query for our log aggregator (which has its own agent). The logs lit up: every memory spike aligned perfectly with a specific System Refresh Event. In a codebase with millions of lines, this “obvious” connection became visible only because we knew exactly where to look.

Deep Dive with a Conversational Profiler

The crash happened deep in our core infrastructure—battle‑tested logic that required surgical precision to modify. Instead of manually sifting through heap snapshots, I used a Model Context Protocol (MCP) to turn the profiler into a conversational partner.

AI: I detect a high volume of duplicate objects on the heap.
Me: That’s impossible, those should be cached and reused.
AI: The cache references are unique. They are not being reused.

Guiding the AI and filtering out hallucinations allowed it to surface a race condition I had examined many times but never truly seen.

Root Cause: The “Stampede”

The issue was a classic stampede: clearing old data before the new data was ready. The fix concept was a “Relay Race” pattern—ensuring a safe handoff between versions of cached data.

Implementing the Fix with AI Assistance

Prompt

Refactor this cache logic to support a “Versioned Handoff”. Ensure thread safety during the swap between Version 1 and Version 2.

Result
The AI generated the boilerplate for an atomic swapping mechanism. I didn’t copy‑paste blindly; instead, I set up an “AI Tribunal” (GitHub Copilot for logic, Claude for code, Gemini for architecture) and performed a rigorous human code review to verify the locking mechanism before any staging deployment.

Lessons Learned

• Multiply, don’t replace: Use AI for grunt work—parsing data, generating boilerplate—while you focus on semantics.
• Orchestrate, don’t just chat: Connect your tools. Let metrics talk to logs, and let the profiler talk to code.
• Respect the “boring” solution: The fix wasn’t a fancy new framework; it was a simple, reliable Relay Race pattern.

Conclusion

The case is finally closed. The fires are out, and production is quiet again—exactly how a well‑engineered system should feel.