Operations | Monitoring | ITSM | DevOps | Cloud

Large language models don’t work in a vacuum. They often rely on Model Context Protocol (MCP) servers to fetch additional context from external tools or data sources. MCP provides a standard way for AI agents to talk to tool servers, but this extra layer introduces complexity. Without visibility, an MCP server becomes a black box: you send a request and hope a tool answers. When something breaks, it’s hard to tell if the agent, the server or the downstream API failed.

In another post in this series, we discussed how to instrument large language model (LLM) calls. This can be a good starting point, but generative AI workloads increasingly rely on agents, which are systems that plan, call tools, reason, and act autonomously. And their non‑deterministic behavior makes incidents harder to diagnose, in part, because the same prompt can trigger different tool sequences and costs.

Moving a large language model (LLM) application from a demo to a production‑scale service raises very different questions than the ones you ask when playing with an API key in a notebook. In production, you have to answer: How much is each model costing us? Are we keeping latency within our service‑level objectives? Are we accidentally returning hallucinations or toxic content? Is the system vulnerable to prompt‑injection attacks?

The role of AI in observability is evolving rapidly, but the data from our fourth annual Observability Survey makes one thing abundantly clear: the potential is real, and so are the reservations. Practitioners overwhelmingly see value in using AI to help surface anomalies, forecast and spot trends, assist with root cause analysis, and get new users up to speed quicker.

Open source software and open standards are now an essential part of how organizations maintain their systems. That's not to say they haven't always been important, but the fourth annual Observability Survey, brought to you by Grafana Labs, shows just how deeply the shift to open has taken hold, with 77% of respondents saying open source and open standards are important1 to their observability strategy.

Note: This blog post is co-authored by Daniel Shechter, CEO and co-founder of Miggo Security. Modern runtime security is critical to understand complex systems and detect and protect against attacks, especially in rapidly evolving cloud native architectures. For many security teams, however, achieving deep visibility into runtime risks remains a moving target.

The Grafana Drilldown apps gives you a queryless, point-and-click way to explore your metrics, logs, traces, and profiles. But finding an insight is only half the job—you still need to act on it. Previously, that meant leaving Drilldown, manually copying queries, and navigating through Grafana's dashboards, Alerting, and "Explore" interfaces to pick up where you left off.

In today's cloud-native environments, managing resource waste and optimizing costs can feel like a constant battle. Operators, along with their fearless FinOps teams, spend countless hours hunting down unused resources, deciphering complex telemetry data, and manually implementing code or configuration changes to try to reduce cloud costs. But what if you could automate the entire process, from identifying waste to implementing the fix, all based on actual production telemetry?

We're big proponents of OpenTelemetery, which has quickly become a new unified standard for delivering metrics, logs, traces, and even profiles. It's an essential component of Alloy, our popular telemetry agent, but we're also aware that some users would prefer to have a more "vanilla" OpenTelemetry experience.

Ben Avner is the Head of Ecosystem and Strategic Alliances at Apono, where he leads the company’s global partner strategy and technology alliances. He focuses on building and scaling strategic partnerships that drive product innovation, partner-influenced pipeline, and long-term growth. A former founder and engineer, Ben brings a strong technical foundation and a builder’s mindset, combined with experience across marketing, product partnerships, and go-to-market strategy.