Operations | Monitoring | ITSM | DevOps | Cloud

Atlassian’s 2025 State of DevEx Survey reveals a paradox: AI is saving developers time, but they’re still losing time to organizational inefficiencies.

You did it. You added the latest AI agent into your product. Shipped it. Went to sleep. Woke up to find it returning a blank string, taking five seconds longer than yesterday, or confidently outputting lies in perfect JSON. Naturally, you check your logs. You see a prompt. You see a response. And you see nothing helpful. Surprise. Prompt in and response out is not observability. It is vibes.

For today's leading service providers and large enterprises, ensuring peak performance requires navigating a labyrinth of data streams, monitoring tools, and legacy systems. This often leaves network operators spending more time searching for information than acting on it. A new AI-enabled network management is dawning, promising to upend these cumbersome workflows.

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.

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.

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.

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.

‍ We're not witnessing the end of observability, we're witnessing its evolution into something far more powerful. The observability industry is having its Moneyball moment. Just like Billy Beane revolutionized baseball by using data analytics to compete with teams that had vastly larger budgets, observability is undergoing a fundamental transformation.