Operations | Monitoring | ITSM | DevOps | Cloud

Teams deploying LLM applications face a critical blind spot: They can measure speed and cost, but not whether their AI is actually giving good answers. To build user trust in these applications, teams also need to measure response quality, including factual accuracy, safety, and tone. Operational metrics show how a system behaves, but not whether its responses are correct or on brand.

Read a few articles on artificial intelligence and financial operations, and you’re bound to run across a sentence like this: AI enables FinOps teams to reduce TCO and boost ROI. Or one like this: The future of FinOps uses agentic AI-powered systems to detect and remediate cost issues automatically. Keep reading and you’ll find piece after piece that say a lot about AI and FinOps … without really saying anything.

Information architecture used to be about structure. We organized menus and pages into trees, built hierarchies, and created pathways for people to follow. For years, that worked. Navigation was the interface. But that world is changing. People aren’t clicking their way through information anymore. They’re asking for it. They’re refining questions, expecting context, and assuming that systems will not only understand what they mean, but act on it.

The holiday shopping season is a familiar ritual for many. We spend hours researching the best deals, comparing features, and reading reviews to make sure we’re investing in the right things. As we all come to grips with the fact that 2026 is right around the corner, engineering leaders are doing the same thing, but largely in response to the explosion of AI developer tools.

In this guide, we will walk you through how to add a service link that connects directly to the Rollbar Debugging Assistant—our ChatGPT-powered tool that helps you quickly analyze and debug an occurrence’s raw.json without needing access to your code repository. The Rollbar Debugging Assistant makes it easier and faster to understand what went wrong in a specific error occurrence.

Even the best site reliability engineers (SREs) spend too much time doing reactive work—triaging incidents, gathering context, escalating to the right teams, and documenting what happened. That work is essential, but it’s not where an SRE’s highest value lies. These engineers are hired to build and maintain resilient systems, not play air-traffic control with every alert that hits their queue.

We’ve all built the standard RAG pipeline: chunk data, embed it, store it, retrieve it. It works… maybe. Just like LLMs have added a thinking mode for better results, we can add a bit of thinking to our RAG pipelines with agentic RAG using PostgreSQL and n8n.

Reports by Gartner say that over 40% of agentic AI projects may be discontinued by 2027, primarily due to unverified costs, vague business value, and weak risk governance. Most business leaders can already see the risk. Or the opportunity. That’s not the problem; the problem is what happens after - the effectiveness of process execution. Anushree Verma, Senior Director Analyst at Gartner, said: Execution latency is now the most expensive form of operational waste.