Orchestrating Multi-Site Deployments: Daemon Health Monitoring, GA4 Integration, and CloudFront Cache Management

This session focused on three concurrent engineering tracks: validating the health of the jada-agent orchestrator daemon running on AWS Lightsail, establishing authenticated access to Google Analytics 4 data pipelines, and deploying SEO content updates across multiple static sites hosted on S3 with CloudFront distribution layers. Below is a technical breakdown of the decisions made, infrastructure patterns used, and the current operational state.

Daemon Health Monitoring and AWS Lightsail SSH Access

The jada-agent.service runs on a Lightsail instance at 34.239.233.28 and is responsible for consuming tasks from a progress dashboard and executing multi-turn agent sessions. Rather than storing the private SSH key locally (which creates security debt), the workflow leveraged AWS Lightsail's temporary SSH credentials API.

Why this approach: The Lightsail API endpoint returns time-limited credentials paired with a temporary public key that can be added to the instance's authorized_keys without permanent key management overhead. This reduces blast radius if the local machine is compromised.

Command pattern used:

# Retrieve temporary SSH credentials via Lightsail API
aws lightsail get-instance-access-details \
  --instance-name jada-agent \
  --region us-east-1

# Write temp key, connect, and collect metrics
ssh -i /tmp/lightsail-temp-key ubuntu@34.239.233.28 \
  "systemctl status jada-agent.service && journalctl -u jada-agent.service -n 100"

Health findings: The daemon has been running continuously for 11 days with no system-level issues. CPU utilization averaged 0.65% (within expected bounds for a 60-second polling loop), memory consumption was 144MB of 914MB available, and status checks showed zero failures over the last 2 hours. Load average hovered near zero between task executions, indicating proper idle behavior.

However, two of three agent sessions today hit Claude's 30-turn limit and exited with code 1. While the daemon logs these as errors, they are not crashes—the service remains stable. One session (Session 2) completed successfully and generated meaningful output (a needs-you task regarding e-signature link blockers and crew page generator code). The recurring max-turn exits suggest task complexity may need scoping adjustments, but this is a tuning issue, not a stability issue.

Critical issue identified: The `port_sheet_sync.py` script's Google OAuth token is expired or revoked. Every 30-minute sync attempt since at least mid-afternoon has returned HTTP 400 Bad Request. Port sheet syncs are currently offline and require token re-authentication.

Google Analytics 4 Authentication and Data Pipeline Setup

To enable automated GA4 reporting and analysis, the session established authenticated access to Google Analytics 4 properties using OAuth 2.0 credentials already present in the jada-agent's secrets store.

Architecture decision: Rather than creating separate GA4 service accounts, the workflow reused existing OAuth 2.0 credentials (client_id and client_secret) stored in the secrets directory for the dangerouscentaur@gmail.com account. This reduces credential sprawl and leverages the existing trust relationship with Google's OAuth provider.

File created: `/Users/cb/Documents/repos/tools/auth_ga.py`

This script handles the OAuth 2.0 authorization flow for GA4 and caches tokens to avoid repeated authentication round-trips. The implementation validates that the google-auth-oauthlib library is available and confirms client credentials exist before attempting token refresh.

Usage pattern:

python3 ~/Documents/repos/tools/auth_ga.py --account dangerouscentaur@gmail.com

Once authenticated, the pipeline can enumerate all GA4 properties under the dangerouscentaur account and retrieve 7-day reporting windows. During this session, a full 7-day GA4 report was pulled for the 86dfrom.com property to establish baseline metrics before deployment.

Why this matters: Automated GA4 data access enables the daemon to generate analytics-driven task suggestions and monitor site performance post-deployment without manual dashboard inspection.

Multi-Site Static Content Deployment and SEO Optimization

Three sites underwent content and configuration updates during this session:

• 86from.com (formerly 86dfrom.com): Directory renamed to match corrected domain naming, new SEO page created at `/site/what-does-86d-mean`, and index.html updated with structured content.
• sailjada.com: Multiple iterative edits to index.html (16+ edit cycles), suggesting refinement of layout, copy, or booking widget integration.
• queenofsandiego.com: BookingAutomation.gs script updated twice, likely to fix booking widget rendering or event handling.

Critical technical fix on 86from.com: The booking widget JavaScript was using Handlebars-style double braces (`{{` and `}}`), which conflicted with the page's template engine. A careful search-and-replace was executed to convert only the braces within the booking widget section (lines within the explicit widget `

# Deploy 86from.com to S3
aws s3 sync /Users/cb/Documents/repos/sites/86from.com/ \
  s3://sailjada-www/ --delete

# Invalidate CloudFront distribution cache
aws cloudfront create-invalidation \
  --distribution-id [DIST_ID_FOR_86FROM] \
  --paths "/*"

# Deploy staging version for pre-flight validation
aws s3 sync /Users/cb/Documents/repos/sites/86from.com/ \
  s3://sailjada-staging/ --delete

aws cloudfront create-invalidation \
  --distribution-id [STAGING_DIST_ID] \
  --paths "/*"