Operations | Monitoring | ITSM | DevOps | Cloud

AI agents for SREs were supposed to slash mean time to resolution and eliminate alert fatigue. Instead, most teams got expensive, unreliable tools that burn through tokens without delivering insights. But what if the problem isn't the AI models themselves? Recent benchmarking reveals the real bottleneck: context engineering. When we tested our context engineering approach against conventional methods, the results were dramatic: Scroll down for our benchmark results to see the full comparison.

This blog was authored by Jason Bloomberg, Managing Director, Intellyx BV ‍ Observability depends upon telemetry – the data streaming from various applications, services, and systems that indicate their internal state in real-time. Various tools consume such telemetry to enable both operational and cybersecurity tasks.

Imagine a developer at a fast-growing company. A customer support agent reports a critical issue: a user's recent order is stuck in a "pending" state. The agent provides a customer ID and a request ID. The developer's typical process is a familiar, painful dance: This process is slow, tedious, and prone to human error. The Mean Time to Resolution (MTTR) is measured in hours, not minutes, and it's a huge drain on engineering resources.

We're thrilled to announce the launch of our new AI Chatbot, now live and in production for all users. This isn't just a simple Q&A bot; it's a powerful assistant designed to streamline your workflow, provide instant context, and help you navigate the Mezmo platform more efficiently.

Building a great developer experience is about more than just the code. It’s about creating a unified ecosystem where your tools work together seamlessly. That’s been the vision behind our work on the Mezmo MCP Server, and I’m excited to share it with you. At its core, the MCP Server is a universal remote for your data pipeline.

For years, the observability industry has been obsessed with one thing: data volume. We've built incredible pipelines, optimized agents, and scaled storage to handle petabytes of logs, metrics, and traces. The promise was simple: collect more data, get more visibility. But we've hit a wall.

Originally published on Medium, this piece by Winston Hearn dives into a philosophical discussion on why the "data is oil" metaphor is no longer serving the tech industry. Hearn argues that by reframing our thinking to "data is power," we can better understand and manage today's complex data systems. ‍ For more than a decade, we in the tech industry have referenced a common metaphor: data is the new oil. It’s a concept that’s easy to grasp.

The rise of platform engineering has put a new team at the center of the developer experience. These teams are tasked with building the "paved road" for developers, which includes providing a robust, self-service observability stack. However, they face a dual mandate: provide a great developer experience and manage the ever-growing costs and complexity of the tools involved.

It is 12PM and you just start eating lunch when your phone starts buzzing. A storm of different monitoring and system-level alerts start stacking up on your phone and slack. The incident response "war room" opens and downtime communications are being drafted to customers. Your team is under pressure to find the root cause, but you are immediately hit with roadblocks.

Dynatrace is a powerhouse for application performance monitoring and business analytics. But for many organizations, its power comes with a significant challenge: as applications scale across complex hybrid environments and diverse tech stacks, the sheer volume and variety of logs, metrics, and traces sent to the platform can explode, leading to staggering and unpredictable costs.