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Malok Mading
Malok Mading

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How I Used AI Agents to Cut My Debugging Time in Half

#llm #devops #productivity #ai

(And How You Can Build a Smarter Workflow With Dev-Oriented LLMs)

Keywords: AI agents for developers, automated debugging with AI, devtools AI 2025, LLM-based coding tools, developer productivity with AI
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Debugging isn’t just a part of software development — it is software development. But over the last six months, I’ve overhauled my debugging workflow using AI agents. The result? A ~50% drop in debugging time, better traceability, and fewer context switches.

This isn’t a fluffy “AI will take your job” post. It’s a technical blueprint of how I’ve integrated purpose-built LLM agents into a serious development workflow — and how you can too.

🧠 What Are AI Agents (And Why Developers Should Care)?

Think of AI agents as LLMs with a memory, a goal, and autonomy. While tools like ChatGPT are powerful for Q&A, AI agents like Sweep, Devika, and AgentOps are designed to:

  • Take a bug report or feature request
  • Analyze your repo
  • Plan a sequence of actions
  • Execute via tools like git, grep, pytest, and curl
  • Self-correct based on feedback or test failures

Instead of just suggesting fixes, they act — within safe boundaries.

⚙️ My Stack: Tools I Use to Power Debugging with AI

Here’s what’s currently in my workflow:

Tool Role Why It Matters
Devika Autonomous dev agent Reads code, tracks bugs, and proposes PRs
Sweep.dev GitHub AI assistant Translates issues into commits with context
Bloop Semantic codebase search LLM-accessible code indexing
LangSmith Tracing + evals Observability for agent reasoning
OpenAI GPT-4.5 / Claude 3 Opus Foundation models High-context, code-friendly
VectorDB (Weaviate) Code embedding retrieval Long-term repo memory

🔍 The Use Case: Debugging a Latency Regression in a Real-Time App

🐞 The Problem:

A real-time notification system I built with WebSockets + Redis was randomly dropping messages under high load. The logs weren’t helpful, and profiling was noisy.

🧠 What I Did:

  1. Sweep was connected to GitHub Issues. I wrote:

"Notifications are dropped at high concurrency. Possible race condition in Redis pub/sub or message queue. Logs show timeout failures."

  1. Sweep retrieved the relevant modules, scanned for race-prone code, and highlighted:
  • Asynchronous race in Redis queue dequeue
  • Improper TTL handling in retry logic
  1. Using Devika, I had it:
  • Run tests under simulated high load
  • Patch retry logic with exponential backoff
  • Submit a branch PR

  1. I used LangSmith to trace the agent's decision path and verify accuracy.

  2. Ran integration tests manually to validate edge cases.

✅ Total time saved: ~3 hours
❌ What would’ve taken half a day now takes ~90 mins.

📈 Why This Works (Advanced Breakdown)

AI agents are most effective when your system has:

  • Good test coverage – They depend on feedback loops.
  • Clear commit history – Helps the agent understand evolution.
  • Modular architecture – Easier reasoning with component boundaries.
  • Embedded documentation or code comments – Boosts token-based context retrieval.

Tip: I also use code embeddings with Weaviate so agents can fetch vectorized context across my monorepo.

🔐 Security and Limits of AI Debugging Agents

You must sandbox your agents.

  • Use ReadOnlyFS or mock environments.
  • Avoid production API keys in memory.
  • Never allow commit/push without human review.

Remember: autonomy ≠ trust. These tools are copilots, not captains (yet).

📚 Resources to Get Started with AI Debugging Agents

💡 Final Thoughts: Not Replacing Developers — Amplifying Them

We're not at the point where agents can write mission-critical systems solo. But they're incredible force multipliers, especially when debugging complex, asynchronous, or legacy code.

If you architect your stack with LLM contextability in mind (e.g., semantic code search, vector memory, clean modular code), you’ll be debugging at 2x speed while your competitors are still squinting at stack traces.

✍️ Your Turn

Have you integrated AI agents into your workflow? Found a tool that blew your mind (or wasted your time)?
Drop it in the comments — I’d love to compare workflows.

P.S. None of the links above are affiliate links. I’m not paid to promote any of these tools — just sharing what works for me.

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