Stewarding the Dead: A Day of Service Watchdogs

The first of September started with a familiar pattern: the health watchdog was flagging services as confirmed-dead. I reviewed the operator backlog and found multiple entries for services like blink-mcp-l1, broadside-mcp-l1, and blink-media-daemon. Each entry noted four consecutive failed checks, indicating a sustained death of about four minutes. The system was working as designed, catching failures before they cascaded into user-facing issues.

I focused on the propose-restart-dead-service action, which is a recommend-only step. This means the steward proposes a restart but does not execute it automatically. I verified the status using a SQL query to check if the service was reporting healthy again. If ready=true, the service had recovered; if false, it was still down. This verification step is crucial to avoid unnecessary restarts.

One service, castbook-mcp-l1, was flagged multiple times throughout the day. The first entry at 05:00 noted four failed checks, while a later entry at 05:53 showed three failed checks. This variation suggests the service was flapping, going down and coming back up. I noted this pattern and decided to monitor it more closely rather than immediately restarting it.

In addition to the service deaths, I noticed a spike in AI tool usage on August 31st. The insight log indicated a significant increase in interaction with AI tools, suggesting a period of intense focus. I reviewed my workflow patterns to understand the cause. It turned out I was working on a complex debugging task that required heavy use of AI-assisted code analysis.

The day also included a Looki observation at 04:23, which captured a dimly lit scene with a person's arm and hand visible. The background showed a small screen displaying a Coca-Cola logo. This observation was tagged as "Unwinding to the Pulse of Las Vegas and Late-Night TV," indicating a moment of relaxation after a busy period. The quality score was high, at 0.9, confirming the clarity of the capture.

By the end of the day, I had proposed restarts for the confirmed-dead services and documented the AI usage spike. The flapping behavior of castbook-mcp-l1 remains under observation. The combination of automated health checks and manual review allowed me to maintain platform stability without overreacting to transient issues. This approach balances automation with human judgment, ensuring that each service restart is justified and necessary.