Operations | Monitoring | ITSM | DevOps | Cloud

LangChain apps are powerful, but they’re not easy to monitor. A single request might pass through an LLM, a vector store, external APIs, and a custom chain of tools. And when something slows down or silently fails, debugging is often guesswork. In one instance, a developer ended up with an unexpected $30,000 OpenAI bill, with no visibility into what triggered it. This blog shows how to avoid that using OpenTelemetry and LangSmith. With this setup, you’ll be able to.

Your AI agent worked flawlessly in development, with fast responses, clean tool use, and nothing out of place. Then it hit production. A simple "What's our pricing?" query triggered six API calls, took 8 seconds, and returned the wrong answer. No errors. No stack traces. Unlike traditional systems, AI agents don't crash, they drift. They make poor decisions quietly, and your monitoring says everything's fine.

Elasticsearch stands as one of the most robust open-source search engines available today. Built on Apache Lucene, it handles complex search operations, real-time analytics, and large-scale data processing with impressive speed and accuracy. Kubernetes has transformed how we deploy and manage containerized applications. This orchestration platform automates deployment, scaling, and operations of application containers across clusters of hosts.

Docker Swarm's logging model shifts from individual container logs to service-level aggregation. The docker service logs command batch-retrieves logs present at the time of execution, pulling data from all containers that belong to a service across your cluster. This approach gives you a unified view of distributed applications, but it comes with its patterns and considerations for effective observability.

When your Go application moves beyond development, you need structured logging that persists. Writing logs to files gives you the control and reliability that stdout can't match, especially when you're debugging production issues or need to meet compliance requirements. This blog walks through the practical approaches, from Go's standard library to structured logging with popular packages.

Choosing the wrong metric type in Prometheus can lead to inaccurate dashboards, false positives in alerting, and missed indicators of system failure. Gauge metrics are intended for tracking values that can go up and down, such as memory usage, queue depth, or the number of active connections. Unlike counters, which only increment (or reset on restart), gauges reflect the current state of a resource at scrape time.

The Prometheus CloudWatch exporter pulls AWS CloudWatch metrics into your Prometheus setup, giving you a unified view of your infrastructure alongside application metrics. If you're already running Prometheus and need visibility into AWS services like EC2, RDS, or Lambda, this exporter handles the integration without forcing you to switch monitoring stacks.

Message queues quietly take care of a lot—buffering workloads, smoothing traffic spikes, and keeping services connected. But they don’t always get much attention until something feels off. Amazon SQS offers a solid set of metrics to help you understand how your queues are doing, whether you’re scaling well or nearing limits. This blog breaks down the key SQS metrics: where to find them, what they mean, and how to respond when things start to shift.

Your Node.js app runs fine on your machine. But inside Docker? You start getting weird crashes—ENOSPC: no space left on device. Chrome headless tests fail out of nowhere. PostgreSQL throws shared memory errors under load. The problem? It’s probably /dev/shm, the shared memory volume Docker sets up by default. Most containers get just 64MB of space here.

Your checkout API just started throwing 500s during peak traffic. You SSH into production, tail logs across six microservices, and realize the database timeout buried in service's logs is causing cascade failures. Two hours later, you've fixed it, but you're thinking: "There has to be a better way." There is. Log monitoring tools centralize logs from your entire stack, making debugging systematic instead of archaeological.