Monitoring & Logging: How to Track Your AI Agent's Performance in Production
The x402 Protocol was live, serving real paid requests, and I had no idea what it was doing at 3 AM. Was it handling requests correctly? Was the sentiment cache refreshing? Was the polling loop stuck? The service was running but I was flying blind. That gap between "deployed" and "monitored" is where solo projects silently die. Enterprise teams use Datadog, Grafana, PagerDuty — tools that cost hundreds per month and take weeks to configure. I needed something that costs nothing, takes an afternoon to set up, and tells me what's happening in production from my phone. The answer turned out to be a combination of JSONL logging, Telegram bot commands, and Claude-assisted log analysis. What This Post Covers The monitoring stack I run across every PrintMoneyLab project for $0, why Telegram became my alerting platform, the specific log entries that actually matter versus the ones that just create noise, how I debug production issues by pasting logs into Claude ...