I Ran Claude Code in a Ralph Loop for 48 Hours. Here's What Happened.

Last month, I decided to test the limits of autonomous AI development. The setup: a real client project, a detailed PRD, and Claude Code running in a Ralph Loop for 48 straight hours.

This isn't a synthetic benchmark. It's what happened when I let the AI loose on a real codebase with real requirements. The good, the bad, and the lessons learned.

The Setup

The Project

A Swiss logistics company needed an internal tool for managing their warehouse operations. The requirements included:

• Inventory management with barcode scanning
• Order picking optimization
• Real-time stock level monitoring
• Integration with their existing ERP system
• Role-based access control
• Reporting and analytics dashboard

Not a trivial project. The PRD had 47 discrete tasks across these areas.

The PRD Structure

I structured the PRD with clear completion criteria for each task:

## Task 15: Implement Order Picking Algorithm

### Requirements
- [ ] Create picking route optimization algorithm
- [ ] Minimize warehouse traversal distance
- [ ] Support partial order fulfillment
- [ ] Handle priority orders
- [ ] Log all picking decisions for audit

### Acceptance Criteria
- Algorithm completes in <100ms for orders up to 50 items
- Route optimization reduces average travel by >30% vs naive approach
- All edge cases have test coverage

### Tests
- test_picking_basic.py
- test_picking_edge_cases.py
- test_picking_performance.py

Every task had this structure: requirements, acceptance criteria, and specified tests.

The Loop Configuration

#!/bin/bash
MAX_ITERATIONS=200
LOG_FILE="ralph_log_$(date +%Y%m%d_%H%M%S).txt"
ITERATION=0

while [ $ITERATION -lt $MAX_ITERATIONS ]; do
  echo "=== Iteration $ITERATION at $(date) ===" | tee -a $LOG_FILE

  # Check completion
  PENDING=$(grep -c "\- \[ \]" docs/prd.md)
  DONE=$(grep -c "\- \[x\]" docs/prd.md)
  echo "Tasks: $DONE complete, $PENDING pending" | tee -a $LOG_FILE

  if [ $PENDING -eq 0 ]; then
    echo "All tasks complete!" | tee -a $LOG_FILE
    exit 0
  fi

  # Run Claude Code
  claude --print "
  Read docs/prd.md. Find the first task with unchecked requirements.

  1. Understand the task requirements and acceptance criteria
  2. Read relevant existing code before making changes
  3. Implement the requirements
  4. Write or update tests as specified
  5. Run tests with pytest
  6. If tests pass, mark requirements as done and commit
  7. If tests fail, analyze and fix

  Be thorough. Read before writing. Test before committing.
  " 2>&1 | tee -a $LOG_FILE

  ITERATION=$((ITERATION + 1))
  sleep 5
done

I kicked it off Friday evening and let it run through the weekend.

Hour by Hour: What Actually Happened

Hours 0-6: Strong Start

The first six hours were impressive. Claude Code:

• Set up the project structure
• Created the database schema
• Implemented basic CRUD operations for inventory
• Added authentication with JWT
• Built the initial API endpoints

By hour 6, 12 tasks were complete. The code was clean, tests were passing, and commits were well-structured.

Key observation: The AI worked methodically. It read existing code before making changes. It ran tests after every modification. The fresh context on each iteration meant it wasn't carrying forward any confusion.

Hours 6-18: Steady Progress

The middle section was where the bulk of the work happened:

• Order management system
• Picking list generation
• Real-time stock updates via WebSocket
• Initial dashboard components

By hour 18, 28 tasks were complete. The pace had slowed—tasks were getting more complex—but progress was consistent.

First significant failure at hour 14: The AI got stuck on the picking algorithm. It kept generating solutions that passed basic tests but failed the performance benchmark (<100ms for 50 items). It iterated 8 times before I noticed.

Human intervention #1: I added a hint to the PRD: "Consider using a traveling salesman approximation algorithm like nearest neighbor." Next iteration, solved.

Hours 18-30: The Complexity Wall

This is where things got interesting. The remaining tasks involved:

• ERP integration (external API calls)
• Complex business logic (priority handling, partial fulfillment)
• Cross-cutting concerns (audit logging, permissions)

Stuck at hour 22: The ERP integration task. The AI couldn't figure out the authentication flow without seeing the actual API documentation. It made reasonable guesses, but they were wrong.

Human intervention #2: I added the ERP API documentation to the codebase. The AI read it on the next iteration and implemented the integration correctly.

Stuck at hour 26: Permission system. The requirements were complex:

• Warehouse managers can see all inventory
• Pickers can only see their assigned zones
• Admins can modify system settings
• Auditors have read-only access to everything

The AI implemented something, but it didn't match the requirements. The tests it wrote passed, but the tests didn't actually validate the requirements correctly.

Human intervention #3: I rewrote the tests to properly validate the permission logic. The AI then fixed the implementation to match.

Hours 30-42: Refinement and Edge Cases

With the major features done, the remaining tasks focused on:

• Edge case handling
• Error messages and user feedback
• Performance optimization
• Documentation

This phase went smoothly. The AI excelled at:

• Adding error handling to existing code
• Writing docstrings and comments
• Optimizing database queries based on test results
• Generating API documentation

By hour 42, 41 of 47 tasks were complete.

Hours 42-48: The Final Six

The remaining 6 tasks required human judgment:

1. Dashboard layout decisions - Where should each widget go? The AI made choices, but they weren't what the client wanted.
2. Business rule clarification - "Priority orders should be handled first" - but what constitutes priority? The PRD wasn't specific enough.
3. ERP edge cases - The ERP sometimes returned malformed data. The AI couldn't anticipate this from the documentation.
4. Performance tuning - The analytics queries were slow. The AI optimized, but the real fix required understanding the client's data patterns.
5. Reporting format - "Export to Excel" - but which columns? What order? What formatting?
6. Final integration testing - Some workflows failed when combined in ways the unit tests didn't cover.

These 6 tasks took me another 4 hours of focused work to complete.

By the Numbers

Cost Analysis

• Claude API costs: ~$180
• Human time (6 hours at my rate): ~$1,200
• Total: ~$1,380

Traditional estimate for this project: 2-3 weeks of developer time, roughly $15,000-$25,000.

Cost reduction: 90%+

Lessons Learned

1. PRD Quality is Everything

The tasks that completed smoothly had precise requirements. The tasks that got stuck had ambiguity.

Bad requirement: "Handle priority orders appropriately" Good requirement: "Orders marked as priority=true must be processed before all non-priority orders in the same zone, regardless of creation time"

Invest time in the PRD. It pays off exponentially.

2. Tests Are Your Specification

When the AI wrote its own tests, they sometimes didn't actually test what they claimed to test. The tests passed, but the feature was wrong.

For critical functionality, write the tests yourself. Or at least review the tests the AI generates before assuming they validate correctly.

3. External Integrations Need Documentation

The AI couldn't guess how the ERP API worked. It needed documentation. If your project involves external systems, include their API docs in the context.

4. Fresh Context Has Limits

The Ralph Loop's strength—fresh context each iteration—is also a weakness. The AI doesn't remember what it tried before. Sometimes it makes the same mistake repeatedly.

Solution: Log failures and include them in the next iteration's context, or add hints to the PRD when you see repeated failures.

5. Human Judgment is Still Required

87% autonomous completion is impressive, but that remaining 13% required human expertise:

• Understanding unstated requirements
• Making design decisions
• Handling truly novel situations
• Validating that "correct" is actually correct

The AI amplifies human capability. It doesn't replace human judgment.

Would I Do It Again?

Absolutely. But with adjustments:

1. More upfront PRD work - The time spent writing detailed requirements is minimal compared to the time saved
2. Earlier human checkpoints - I'd check progress every 6-8 hours rather than letting it run unattended
3. Pre-written critical tests - For complex business logic, write the tests before starting the loop
4. External documentation included - Any system the code needs to interact with should be documented upfront

The Bigger Picture

This experiment convinced me that AI-assisted development isn't about replacing developers. It's about changing what developers do.

In this project, my role shifted from "person who writes code" to "person who defines requirements and validates results." The AI handled implementation. I handled specification and judgment.

That's a different kind of work—arguably harder in some ways—but dramatically more efficient.

For Swiss SMBs looking to build custom tools, this means:

• Faster time to market
• Lower development costs
• More predictable outcomes (when requirements are clear)
• Human expertise focused where it matters most

Try It Yourself

If you want to experiment with Ralph Loops:

1. Start with a smaller project (10-15 tasks)
2. Write detailed requirements for each task
3. Include test specifications
4. Set up logging so you can review what happened
5. Check progress every few hours initially

The technique scales, but start small to learn the patterns.

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