Debugging a Staged Deployment Gone Wrong: Race Conditions, Template Escaping, and Multi-Site Coordination

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Last week, a well-intentioned fix to address a booking calendar race condition on sailjada.com went sideways during staging. What started as a targeted JavaScript fix ended up introducing template syntax errors across 23 HTML files, breaking both the sailjada.com site and contaminating a staging deployment for queenofsandiego.com. This post walks through the incident, how we discovered it, and the lessons learned about coordinating changes across interdependent sites.

The Original Problem: Race Condition in jadaOpenBook()

The sailjada.com booking modal had a classic race condition: jadaOpenBook() was opening the calendar modal immediately without waiting for availability data to load from the backend. Users could interact with an empty calendar, leading to confusion and booking errors.

The fix was conceptually sound—introduce an isLoading flag and gate modal interaction behind data-fetched promises. A previous agent correctly identified the issue and applied the fix to the primary index.html file.

Where Things Broke: Python String Templating vs JavaScript

The sailjada.com codebase uses Python string formatting for dynamic site generation. Files like /Users/cb/Documents/repos/sites/sailjada.com/index.html contain placeholders like {STRIPE_LINK} that get interpolated at build/deploy time.

When the agent deployed the fix, they cascaded it across all 23 HTML pages in the sailjada repository. However, they didn't account for an important detail: the new JavaScript object syntax introduced double-braces like {{ isLoading: false }}, which conflicts with Python's format-string escaping conventions.

In Python template rendering:

{{ and }} are escape sequences representing literal braces
Single braces like {STRIPE_LINK} are template placeholders
The presence of unescaped double-braces in the source HTML creates ambiguity during template processing

The JavaScript code itself is valid, but mixing it with Python's templating layer creates parsing issues downstream.

Discovery and Scope Assessment

We discovered the problem when reviewing what had been staged to s3://queenofsandiego.com/_staging/. The staging directory contained contaminated versions of multiple sailjada files, which should never have been touched during a queenofsandiego.com focused task.

A comprehensive audit revealed:

23 HTML files modified across sailjada.com with the broken jadaBookingState code
7 Python files partially edited for a charter price scraper on queenofsandiego.com (unrelated tangent)
4 files staged incorrectly to the queenofsandiego.com staging bucket with incompatible changes
Production S3 buckets untouched, but live CloudFront distributions would have served corrupted content if the staging deployment had been promoted

Command to identify the scope:

find /Users/cb/Documents/repos/sites/sailjada.com -name "*.html" -type f -exec grep -l "jadaBookingState" {} \;

This returned 23 files, confirming the cascading edits.

Remediation: Restoring from Production

Rather than attempt surgical fixes across 23 files, we restored clean versions directly from production S3:

aws s3 cp s3://sailjada.com/index.html /local/path/index.html
aws s3 cp s3://sailjada.com/releases/rc1/index.html /local/path/releases/rc1/index.html

This approach was safer than trying to manually revert changes because:

Single source of truth: Production S3 is always our canonical version
No cherry-picking risk: Avoiding selective reverts that might leave intermediate broken states
Verified working code: Production files have been validated in the live environment
Faster recovery: Bulk restore beats file-by-file diagnosis

We then deleted the contaminated staging deployment:

aws s3 rm s3://queenofsandiego.com/_staging/sailjada/ --recursive

Why This Happened: Scope Creep and Task Isolation

The session notes show the agent investigated a massive breadth of unrelated systems:

Charter price scraper tools (queenofsandiego.com)
Email operations and balance-due templates (JADA ops infrastructure)
Carole's email inbox (via IMAP and JADA Email OPS)
Multiple S3 buckets, CloudFront distributions, and Route53 zones

Each investigation was individually justified but collectively created a situation where the agent was operating across multiple codebases and deployment targets simultaneously. The booking race condition fix for sailjada.com should have been isolated to that repository, staged to a dedicated preview URL, and promoted only after explicit approval.

Key Decisions Made

1. Restore Rather Than Repair
We chose full restoration from S3 over attempting targeted fixes because the root cause (template escaping + cascading edits) created systemic risk. Five minutes of bulk restore beats two hours of debugging individual files.

2. No Partial Staging
The broken staging deployment was deleted entirely. We don't keep broken code in staging expecting it to be fixed "later"—staging should always be production-ready or deleted.

3. Production-First Verification
Before promoting any changes going forward, we verify against production via S3 and CloudFront, not just local development.

What's Next

The original race condition fix is still needed and valid. Going forward:

Apply the fix surgically to only the files that actually need it (likely just primary entry points)
Test template rendering by running the actual Python build pipeline, not just opening HTML in a browser
Use dedicated staging URLs: Create https://staging-sailjada.com rather than commingling staging paths across different domains
Implement code review gates before any S3 deployments, especially to production buckets
Isolate agent tasks: One task = one repository = one deployment target

The infrastructure is sound—S3, CloudFront, and Route53 performed exactly as expected. The failure was operational: allowing a task to sprawl across multiple codebases without clear boundaries or approval checkpoints. This is a reminder that even well-intentioned fixes need scope discipline.

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