Production AI Agents: Lessons from Running 10 Agents at Scale
Deployed 10 AI agents serving 100K users/day - monitoring, error handling, cost optimization, and scaling strategies that actually work
13 posts
Deployed 10 AI agents serving 100K users/day - monitoring, error handling, cost optimization, and scaling strategies that actually work
Setting up comprehensive monitoring and alerting for production systems using Prometheus, Grafana, and Alertmanager.
Implementing distributed tracing and metrics collection across microservices using OpenTelemetry, Jaeger, and Prometheus.
Building a complete monitoring and alerting stack with Prometheus and Grafana for microservices architecture.
Built advanced Grafana dashboards - variables, annotations, alerts, custom panels. Reduced MTTR from 30min to 5min
Fixed Prometheus high cardinality issue - reduced time series from 10M to 100K (99% reduction). Query performance improved 50x
Monitoring Istio service mesh - traffic metrics, distributed tracing, service dependencies, and debugging microservices with zero code changes
Building highly available Prometheus setup with Thanos - unlimited retention, global queries, and surviving datacenter failures
Configuring Alertmanager for production - routing rules, inhibition, silencing, and integrating with Slack and PagerDuty
How we set up Prometheus and Grafana for monitoring our microservices architecture.
Setting up Elasticsearch, Logstash, and Kibana for centralized logging - collecting logs from 15 microservices and making them searchable
Creating effective Grafana dashboards with Prometheus - from basic graphs to advanced alerting and team dashboards
Setting up Prometheus to monitor 5 microservices - metrics collection, alerting, and our first production incident caught by monitoring