Sail JADA — Engineering Log

Technical work on sailjada.com and the JADA booking systems

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Diagnosing and Resolving OAuth Token Expiration in Multi-Site Orchestration: The jada-agent Daemon Deep Dive

This session involved a comprehensive health check of the jada-agent orchestrator daemon running on a Lightsail instance (34.239.233.28), along with simultaneous infrastructure work across multiple static sites. While the daemon itself remains stable, we uncovered a critical OAuth token expiration in the port sheet synchronization layer that requires immediate attention.

What Was Done

  • Performed remote health diagnostics on the jada-agent.service daemon via AWS Lightsail and SSM Session Manager
  • Analyzed 3 days of daemon logs, metrics, and session activity to establish baseline behavior
  • Identified and documented a persistent Google OAuth token failure in the port_sheet_sync.py component
  • Completed infrastructure work on three static sites: sailjada.com, 86from.com, and queenofsandiego.com
  • Implemented new authentication pattern for Google Analytics API access via auth_ga.py

Daemon Health and Architecture

The jada-agent.service has been running continuously for 11 days with 3 days of current uptime on the Lightsail instance. The daemon operates on a 60-second polling loop, consuming approximately 0.65% CPU and 144MB of RAM on a 914MB available system—well within acceptable parameters. Disk utilization sits at 17% (6.2GB of 39GB used), providing ample headroom for task logs and state files.

The daemon's session management follows a structured pattern: it enforces a maximum of 5 concurrent sessions per rolling UTC day, with each session capped at 30 conversation turns to prevent unbounded execution. This session tracking lives in the progress dashboard, which the daemon polls every 30 seconds for new tasks.

Over the past 24 hours, the daemon has executed three sessions:

  • Session 1 (00:00 UTC): Hit the 30-turn limit (exit code 1), which is logged as an error but does not crash the service
  • Session 2 (00:02 UTC): Completed successfully, processed e-signature and crew page blockers, and created a needs-you task for manual review
  • Session 3 (00:05 UTC): Hit the 30-turn limit again (exit code 1)

After session 3, no new tasks were picked up—the daemon correctly remained idle, awaiting new task creation. This behavior is expected and healthy.

Critical Issue: Google OAuth Token Expiration in port_sheet_sync.py

The primary actionable finding is a broken OAuth token in the port sheet synchronization component. Every 30-minute sync attempt since at least this afternoon has failed with:

[port-sheet] token error: HTTP Error 400: Bad Request

This indicates that the stored Google OAuth token for the port_sheet_sync.py script has either expired or been revoked. The token is likely stored in a credentials file (potentially /path/to/repos/tools/port_sheet_sync_credentials.json or similar), and because it lacks refresh token capability or the refresh token has also expired, the sync daemon cannot automatically recover.

The impact is that any port sheet synchronization tasks have stalled. Port sheet updates—which likely feed into booking automation, crew scheduling, or inventory management—are no longer flowing downstream. This is a blocking issue that requires immediate re-authentication of the Google OAuth flow for that specific script.

Auth Pattern Evolution: auth_ga.py and Multi-Account Analytics Access

In parallel with the daemon diagnostics, we implemented a new Google Analytics authentication utility at /Users/cb/Documents/repos/tools/auth_ga.py. This script establishes OAuth 2.0 credentials for accessing the Google Analytics Data API, enabling programmatic report generation across multiple GA4 properties.

The script was designed to:

  • Support account-level authentication via --account flag (e.g., dangerouscentaur@gmail.com)
  • Reuse existing client credentials stored in the jada token infrastructure to avoid credential duplication
  • Output structured GA4 property listings and pull 7-day aggregated reports for specified domains
  • Store refreshable OAuth tokens for autonomous daemon-based report pulls

The script confirmed access to multiple GA4 properties, including those under the dangerouscentaur account, and successfully pulled full 7-day analytics reports for properties like 86dfrom.com (now renamed to 86from.com).

Static Site Infrastructure Updates

86from.com (formerly 86dfrom.com): We renamed the site directory and deployed updated content to S3 and CloudFront. A new SEO page (/Users/cb/Documents/repos/sites/86from.com/site/what-does-86d-mean) was created and deployed. The booking widget JavaScript was refactored to replace Jinja2-style double-brace template syntax ({{ }}) with single-brace equivalents to prevent template engine conflicts. The versioned file was pushed to the staging bucket with a CloudFront cache invalidation to ensure fresh delivery.

sailjada.com: Multiple index.html revisions were made across 13 edit cycles, likely optimizing page load performance, SEO metadata, or responsive design. Each edit triggered a revalidation cycle to ensure changes propagated through CloudFront's cache layer.

queenofsandiego.com: The BookingAutomation.gs Google Apps Script was edited twice, indicating updates to the booking workflow, webhook handlers, or calendar integration logic.

Key Architectural Decisions

1. OAuth Token Lifecycle Management: Rather than embedding OAuth tokens directly in scripts, we adopted a pattern where tokens are stored centrally (in the jada token infrastructure) and referenced by scripts. This reduces credential sprawl but requires careful handling of token refresh cycles. The port_sheet_sync failure highlights the risk: when a token expires, there's no automatic recovery path.

2. Session Limits as Safety Mechanisms: The 30-turn limit per session prevents runaway AI agent loops. Sessions that hit this limit log an exit code 1 but don't crash the daemon—this is intentional. However, complex tasks that require 30+ turns will fail silently. The workaround is either breaking tasks into smaller subtasks or increasing the turn limit based on typical task complexity.

3. Polling Architecture Over Event-Driven: The daemon uses a 30-second polling loop to check the progress dashboard for new tasks. This is simpler to implement and debug than event-driven architecture but introduces a maximum 30-second latency for task pickup. For the current workload, this is acceptable.

4. Template Engine Isolation: The booking widget refactoring (replacing {{ }} with single braces) reflects a decision to keep template logic strictly separated from JavaScript. This prevents Jinja2, ERB, or other server-side template engines from accidentally interpolating JavaScript expressions, which can lead to XSS vulnerabilities or runtime errors.

What's Next

  • Re-authenticate port_sheet_sync.py: Run the Google OAuth flow for the port sheet sync account, store the refreshed credentials with a valid refresh token, and verify that the next 30-minute sync cycle succeeds.
  • Investigate 30-turn exits: Profile the two sessions that hit the turn limit to determine if task scope should be reduced or the limit increased.
  • Monitor daemon stability: Continue polling CPU,