Operations | Monitoring | ITSM | DevOps | Cloud

Edge computing has been the buzzword of the decade. Everyone is talking about pushing intelligence closer to the edge, but most of that intelligence still needs a map and a flashlight. This week, CRN named LogicMonitor to its 2025 Edge Computing 100, recognizing companies that are actually doing something useful at the edge instead of just hyping it. We are honored. We are also a little amused.

If you’re like most developers, you know that writing code isn’t the bottleneck anymore. AI has made it faster than ever, and chances are you’re already using it. Yet, delivering software is still complex because of everything else you have to manage: fixing vulnerabilities, reducing tech debt, cleaning up feature flags, ensuring test coverage, writing documentation, and the list goes on. That’s why we built Rovo Dev, a context-aware AI agent for developers.

We're proud to share that Elastic has been named a Leader in the IDC MarketScape: Worldwide Observability Platforms 2025 Vendor Assessment (doc, November 2025). We believe this recognition validates our ongoing mission: to deliver an observability platform that is open, extensible, and AI-driven to power full-stack observability that unifies operational and business data at scale, allowing SRE teams to move from detect and resolve problems faster.

Observability works best when everyone who needs visibility can get it without the risk of unintentional changes. Honeycomb’s role-based access control system helps teams strike that balance with a selection of Owner, Member, and Read-Only member roles. This control gives teams more flexibility in how they share access across their organization, helping you scale visibility safely without sacrificing control.

AI in network operations is evolving faster than ever. But while new models and agents are emerging almost daily, they’re often working alone, with each confined to its own context, data, and domain. One model might analyze telemetry, another handles automation scripts, and a third generates summaries or recommendations. Each model might be intelligent on its own, but without a way to share context, they end up thinking in isolation, limiting what they can achieve together.