Operations | Monitoring | ITSM | DevOps | Cloud

The world of software development has fundamentally changed. We've moved from monthly releases to continuous delivery measured in minutes, and the rise of AI means velocity is no longer just a goal—it's a requirement for survival. But this relentless speed has exposed a critical flaw in how we approach observability. The industry relies on a "store first, ask questions later" model where you collect every log, metric, and trace, and then hope to find the root cause when something breaks.

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.