Operations | Monitoring | ITSM | DevOps | Cloud

The Cloudsmith 2025 Artifact Management Report offers timely insights into how engineering and DevOps teams are evolving their approach to software artifact management and software supply chain security. With supply chain attacks on the rise and Generative AI reshaping development practices, teams are reevaluating how they manage, secure, and scale their artifact repository infrastructure.

For on-call engineers responding to alerts, every minute counts. Faster incident response means faster mitigation, reduced downtime, and better customer experience. But even the most finely tuned, meticulously detailed alerts can leave responders scrambling for more information. In order to effectively triage and investigate incidents and set remediation in motion, responders need data to help them contextualize alerts.

Data lineage is the evolutionary history of datasets. More concretely, lineage is metadata that captures the flow and transformation of data in data pipelines, also called the data lifecycle.

As organizations rapidly scale their use of large language models (LLMs), many teams are adopting LiteLLM to simplify access to a diverse set of LLM providers and models. LiteLLM provides a unified interface through both an SDK and proxy to speed up development, centralize control, and optimize LLM-powered workflows. But introducing a proxy layer adds abstraction, making it harder to understand how requests are processed.

When an error occurs, developers need to act quickly. But too often, they’re left searching through stack traces without enough context to understand what happened, who owns the code, or what change may have introduced the issue. This slows down triage, creates inefficient handoffs, and takes time away from building new features.

We’ve covered how to get LangChain traces up and running. But even when everything’s instrumented, traces can still go missing, show up half-broken, or look nothing like what you expected. This guide is about what happens after setup, when traces exist, but something’s off.

When a container misbehaves, logs are the first place to look. Whether you're debugging a crash, tracking API errors, or verifying app behavior—docker logs gives you direct access to what's happening inside. This blog covers the full workflow: how to retrieve logs, filter them by time or service, and set up logging for production environments.

Earlier this year, Auvik released our annual IT Trends Report, spotlighting some of the key changes for network management, MSP, and IT practitioners. We know the market and its ups and downs can have a huge impact on the success of MSPs, so we’re bringing you a roll-up of key statistics and findings related to MSP specifically. Read on to see what we found.

LLM-powered agents are reshaping software, but when they fail, troubleshooting is guesswork. Lumigo’s new AI Agent Observability, now in beta, gives you visibility into the entire lifecycle of your agents, from prompt to response to internal decision logic. Built for modern AI workloads, this feature is designed to help engineers monitor, debug, and optimize agents running on platforms like OpenAI, Anthropic, and open-source models.

When we talk to new Honeycomb users, a few things stand out as sounding downright magical. Sometimes we’ll hear, “Wow, is that a new feature?” and we’ll say that no, it’s been like that for years. Clearly we need to get the word out! This is the first installment of a blog series I’ll be writing, covering areas of Honeycomb that elicit reactions of awe and disbelief from new users.