AI Agent Bug Fixing Workflow: Debug and Fix Production Bugs in Minutes

TL;DR: A three-agent pipeline — Analyzer (Gemini CLI, free), Fixer (Claude Code, $0.10), and Verifier (Claude Haiku, $0.02) — can diagnose and fix most production bugs in 3-5 minutes. Here's the exact workflow, prompts, and real examples.

A production bug report comes in. The clock starts. How fast can you ship a fix?

Traditional approach: Reproduce → read logs → read code → hypothesize → test → fix → review → deploy. Time: 2-8 hours.

Multi-agent AI approach: Paste the error → agents analyze → agents fix → agents verify. Time: 3-5 minutes.

This guide shows you the exact workflow.

Related: AI Agent Code Review Automation · How to Coordinate Multiple AI Coding Agents · Gemini CLI vs Claude Code · AI Agent Task Board

The Three-Agent Bug Fixing Pipeline

Agent Roles

Agent	Model	Role	Cost
Analyzer	Gemini 2.5 Pro	Root cause analysis, trace the error through codebase	Free
Fixer	Claude Sonnet	Write the fix and regression test	~$0.10
Verifier	Claude Haiku	Verify fix doesn't break existing tests	~$0.02

Pipeline Flow

Bug report → Analyzer: Find root cause (free, 30s) → Fixer: Write fix + test ($0.10, 90s) → Verifier: Check fix ($0.02, 20s) → Deploy

Total time: ~2.5 minutes. Total cost: ~$0.12 per bug.

Setup (5 Minutes)

Step 1: Create a Bug Fix Squad

1. Go to ivern.ai/signup — free account
2. Click Create Squad → name it "Bug Fix"
3. Add the three agents with these system prompts:

Analyzer system prompt:

"You are a senior debugging analyst. Given an error report and codebase context: 1) Identify the exact file and line causing the error, 2) Trace the call stack to find the root cause, 3) Identify any related code that could cause similar issues, 4) Suggest the fix approach. Be specific about file paths and line numbers."

Fixer system prompt:

"You are a senior developer fixing a production bug. Given the root cause analysis: 1) Write the minimal fix that addresses the root cause, 2) Add error handling to prevent recurrence, 3) Write a regression test that would catch this bug. Do not over-engineer. Fix the specific issue."

Verifier system prompt:

"You are a QA engineer. Given the original bug report and the proposed fix: 1) Verify the fix addresses the root cause, 2) Check that the fix doesn't introduce new issues, 3) Verify the regression test actually catches the original bug, 4) Check for edge cases the fix might miss."

Step 2: Connect Agents

npx @ivern-ai/agent install --key YOUR_IVERN_KEY --provider gemini
npx @ivern-ai/agent install --key YOUR_IVERN_KEY --provider claude

Step 3: Add Your API Key

In Settings, add your Anthropic key ($5 at console.anthropic.com). Gemini CLI is free with Google auth.

Real Examples

Example 1: Null Reference Error

Bug report: "TypeError: Cannot read properties of undefined (reading 'email') at UserProfile.tsx line 42"

Task created on board:

"TypeError: Cannot read properties of undefined (reading 'email') at src/components/UserProfile.tsx line 42. Happens when user logs out while on profile page. Analyzer: find root cause. Fixer: write fix. Verifier: check it."

Pipeline output:

Phase	Agent	Output
Analyze	Gemini CLI	"Root cause: UserProfile component doesn't handle the case where user becomes null during logout. Line 42 accesses user.email without a null check. The useEffect on line 18 sets up a subscription that fires after the user object is cleared from state."
Fix	Claude Code	Added null guard clause: if (!user) return <LoadingSkeleton />;. Updated useEffect cleanup to unsubscribe on unmount. Added test: "renders loading state when user is null".
Verify	Claude Haiku	"Fix verified: null check on line 28 prevents the error. useEffect cleanup prevents stale subscriptions. Regression test covers the exact scenario. No new issues introduced."

Result: Bug fixed in 2 minutes for $0.12.

Example 2: Database Query Timeout

Bug report: "GET /api/dashboard timing out after 30s when user has >1000 tasks"

Task:

"GET /api/dashboard returns 504 timeout when user has >1000 tasks. Check src/api/dashboard.ts and related queries. Analyzer: find root cause. Fixer: write fix. Verifier: check it."

Pipeline output:

Phase	Agent	Output
Analyze	Gemini CLI	"Root cause: N+1 query in dashboard handler. For each task, it queries the agent table separately (line 67: await prisma.agent.findUnique). With 1000 tasks, this generates 1000 sequential DB queries. Fix: eager-load agents with include: { agents: true }."
Fix	Claude Code	Replaced N+1 query with single query using include: { agents: true }. Added pagination (limit 50, cursor-based). Added test for dashboard with 1000+ tasks.
Verify	Claude Haiku	"Fix verified: single query replaces N+1. Pagination prevents future scaling issues. Test covers the exact scenario. Response time should drop from 30s to <200ms."

Result: Performance bug fixed in 3 minutes for $0.12.

Example 3: Payment Webhook Failure

Bug report: "Stripe webhook returning 500. Customer payments not being recorded."

Task:

"Stripe webhook endpoint POST /api/webhooks/stripe returning 500. Payments not being recorded in database. Check src/api/webhooks/stripe.ts. Analyzer: find root cause. Fixer: write fix. Verifier: check it."

Pipeline output:

Phase	Agent	Output
Analyze	Gemini CLI	"Root cause: webhook handler expects event.data.object.metadata.orderId but Stripe sends it as event.data.object.metadata.order_id (snake_case). The destructuring on line 34 uses camelCase, resulting in undefined. This causes a foreign key constraint violation when inserting the payment record."
Fix	Claude Code	Added case normalization for Stripe metadata keys. Added validation that required fields exist before DB insert. Added error response logging. Wrote test with actual Stripe webhook payload format.
Verify	Claude Haiku	"Fix verified: metadata normalization handles both camelCase and snake_case. Validation prevents silent failures. Test uses realistic Stripe payload. Critical: fix preserves backward compatibility with existing webhook handling."

Result: Revenue-impacting bug fixed in 4 minutes for $0.15.

What This Workflow Catches Well

High Success Rate (90%+ fixed correctly)

Bug Type	Example
Null reference errors	Accessing properties on undefined objects
Type errors	Wrong data types passed to functions
N+1 queries	Sequential DB queries in loops
Missing error handling	Unhandled promise rejections, missing try-catch
Off-by-one errors	Loop bounds, array indexing
Race conditions	Async operations completing in wrong order
Missing validations	API inputs not validated before use

Medium Success Rate (60-80%)

Bug Type	Example
Complex state bugs	Multiple state variables interacting unexpectedly
Timing issues	setTimeout/setInterval race conditions
Cross-browser issues	CSS or API differences across browsers
Memory leaks	Event listeners not cleaned up

Low Success Rate (< 50%)

Bug Type	Why
Infrastructure issues	Network, DNS, CDN, load balancer misconfigurations
Third-party API changes	External service behavior changed
Database corruption	Requires manual data repair
Hardware failures	Cannot be diagnosed through code analysis

Cost Breakdown

Per Bug Cost

Phase	Agent	Cost	Time
Analyze	Gemini CLI	Free	15-45s
Fix	Claude Sonnet	$0.08-0.15	60-120s
Verify	Claude Haiku	$0.01-0.03	15-30s
Total		$0.09-0.18	1.5-3.5 min

Monthly Cost by Bug Volume

Bugs/Week	Monthly Cost
5	~$3
15	~$8
30	~$15
50	~$25

Compare to developer time: 2-8 hours per bug × $50-100/hour = $100-800 per bug. AI agents: $0.12 per bug.

Tips for Best Results

1. Include Error Context

Don't just paste the error message. Include:

• Error stack trace
• Relevant code snippet (or let the agent read the file)
• Steps to reproduce
• Expected vs actual behavior
• Any recent changes to affected files

2. Let the Analyzer Run First

Don't jump straight to fixing. The analysis phase often reveals the real root cause is different from what the error message suggests. The N+1 query example above looked like a timeout issue but was really a query pattern problem.

3. Always Verify

The verification step catches ~15% of fixes that would introduce new issues. It's worth the extra 20 seconds and $0.02.

4. Use Specific File Paths

"Fix the auth bug" → vague, generic fix "Fix the JWT validation in src/middleware/auth.ts line 47" → precise, targeted fix

5. Keep the Pipeline Sequential

Don't run Analyzer and Fixer in parallel. The Fixer produces better results when it has the Analyzer's root cause report as context.

Frequently Asked Questions

Can this handle any programming language?

Yes. The underlying AI models support all major languages. The workflow is language-agnostic — you're providing error context and code, and the agents analyze and fix it.

What about security-sensitive code?

For security-critical fixes (auth, payments, encryption), always have a human review the AI's proposed fix before deploying. The AI might not know your organization's security policies.

Does this work with microservices?

Yes, but you need to provide context from multiple services. Include the relevant code snippets from each service involved in the bug's call path.

What if the fix is wrong?

Recreate the task with the output from the first attempt and add: "The previous fix didn't work because [reason]. Try a different approach." The agents will use the failure as additional context.

How does this compare to GitHub Copilot for debugging?

Copilot suggests fixes inline as you code. Multi-agent debugging is a structured pipeline: analyze → fix → verify. The pipeline approach catches more issues because each agent specializes in its phase and the verifier double-checks the fix.

Get Started

1. Sign up free at ivern.ai/signup
2. Create a Bug Fix squad with Analyzer, Fixer, and Verifier
3. Connect Gemini CLI and Claude via terminal
4. Paste your next bug report into a task
5. Get a verified fix in 3-5 minutes

Stop spending hours on bugs. Start fixing them in minutes.

Set up your bug fixing squad →