Operations | Monitoring | ITSM | DevOps | Cloud

AI coding assistants like Claude Code are becoming core parts of modern development workflows. But as with any powerful tool, the question quickly arises: how do we measure and monitor its usage? Without proper visibility, it’s hard to understand adoption, performance, and the real value Claude brings to engineering teams. For leaders and platform engineers, that lack of observability can mean flying blind when it comes to understanding ROI, productivity gains, or system reliability.

When debugging containerized applications in Kubernetes, kubectl logs serves as your primary command-line tool for accessing container logs directly. Understanding how to effectively retrieve, filter, and analyze logs becomes essential for maintaining application health and resolving issues quickly, especially in multi-container environments where correlation across services can make or break your troubleshooting efforts.

In Part 1 of this series, we explored how to instrument a LangChain trip planner agent with OpenTelemetry and send telemetry data to SigNoz. By tracing each step of the planning process: LLM reasoning, tool calls for flights, hotels, weather, and activities, and the final itinerary response, we saw how observability turns a black-box agent workflow into a transparent, debuggable system.

LangChain has become one of the most popular frameworks for building LLM-powered applications, making it easier to create agents that can reason, plan, and take actions. But like any production-grade AI app, LangChain agents can run into performance bottlenecks, hallucinations, or tool call failures. And without proper LangChain observability, it’s hard to know where things break down.

Okay, picture this: if you drew a Venn diagram of folks in tech right now, it'd probably look something like this: You'll probably find yourself in one of those circles, right? I’m guilty of falling in the intersection! Because let's be real, the 'will AI replace developers by 20xx?' debate is everywhere – Reddit, Hacker News, team Slack and even your local cafe. Well, we decided to go straight to the source.

LlamaIndex has become a popular choice for building Retrieval-Augmented Generation (RAG) applications, helping developers seamlessly connect large language models with private or domain-specific data. But RAG workflows can be complex with slow retrieval times, irrelevant or inconsistent responses, and silent failures in the data pipeline can all degrade the user experience. That’s why observability is essential.

“Developers hate marketing.” Do they, really? I often hear this thrown around on podcasts about DevTools marketing, and while it’s true that developers don’t respond to the same old marketing tactics, they do respond to genuine communication. The reason developers are hard to “market” to is that they are also the builders of the stuff you want to sell.

Redis is a powerful tool, but its position in the critical path of applications means that performance issues can have a widespread impact. Whether you use Redis as a cache, session store, or primary database, effective monitoring is essential to prevent slowdowns and ensure a responsive user experience. This guide provides a comprehensive walkthrough of Redis monitoring, covering the essential metrics you need to track, the tools available to you, and the best practices to adopt in 2025.

Almost every other day, when I scroll past r/devops or r/sre, I see a post like this asking how a dev can get started with devops, observability, etc. Sample Reddit thread on how to get started with OTel This blog is an attempt for anyone lost to find their way into observability and a wake-up call for devs to they should think about observability more actively today than ever before. A dev’s observability playbook.

LLM-powered apps are growing fast, and frameworks like the Vercel AI SDK make it easy to build them. But with AI comes complexity. Latency issues, unpredictable outputs, and opaque failures can impact user experience. That’s why monitoring is essential. By using OpenTelemetry for standard instrumentation and SigNoz for observability, you can track performance, detect errors, and gain insights into your AI app’s behavior with minimal setup.