Operations | Monitoring | ITSM | DevOps | Cloud

For the last decade, Gremlin has helped Fortune 500 organizations with critical uptime requirements proactively uncover reliability risks and prevent costly outages. We started with Chaos Engineering, then built Reliability Management to help teams standardize and scale their testing efforts. Today, we take another leap forward with the release of Reliability Intelligence. Reliability Intelligence draws on Gremlin expertise with each test to show you what happened and recommend remediation.

Rural America is experiencing a dramatic surge in network demand driven by high-bandwidth applications like 4K video streaming, real-time sports content, and AI workloads. As broadband competition and digital transformation accelerate, service providers must rethink middle-mile network architecture to be scalable, technology-agnostic, and service-aware.

Traditional observability tools can’t keep up with modern complexity. Dashboard and alert-based approaches still rely heavily on manual processes, resulting in longer troubleshooting cycles, slower decisions, and higher MTTR. Engineering teams need something better. Today we’re launching Open 360 AI, the first observability platform designed for both humans and AI agents working together.

In a modern DevOps environment, having just metrics or just logs is like trying to navigate with half a map because you’re missing important context that makes decisions faster and smarter. Metrics tell you what is happening (CPU spikes, request rates, failed logins) but logs tell you why it’s happening, with the timestamps to prove it.

If your challenger bank is built on composable core platforms like Mambu or Temenos, this one’s for you. Composable platforms enable API-first integration with modular services, letting you launch, adapt, and grow products quickly. That makes API health a top priority — and it shows in our State of API Reliability Report 2025 (we’ve pulled out the key fintech findings for APAC below).

At incident.io, speed is essential. Our product is growing faster than ever; in scope, range of features and the number of people contributing to it. In the early days, when you’re a small startup with just a few hundred endpoints, a basic API setup gets you by. But as things scale, you need to make creating endpoints easy, fast, and reliable.

A trace always remembers! Okay, okay. I will try to keep my Game of Thrones references to a minimum throughout this post, but there is a lot of truth to that statement. In observability, a trace is the “when” and “where” of telemetry signals, allowing us to track the state of interactions between services within a microservice architecture. This makes traces the ideal observability signal for discovering bottlenecks and interconnection issues.