Operations | Monitoring | ITSM | DevOps | Cloud

In the world of cloud-native infrastructure, complexity is the silent killer of innovation. For Cisco Outshift, the company’s incubation engine, managing a sprawling environment of AWS EKS clusters and edge-based MicroK8s workloads created a classic bottleneck: the Platform Engineering team was drowning in toil. Facing SRE burnout and the limits of human scaling, Cisco embarked on an ambitious journey to evolve its internal operations from standard DevOps to Agentic AI.

The way we work has fundamentally changed. Remote and hybrid work aren't temporary shifts; they're the new reality for most organizations. And with that reality comes a challenge IT teams know all too well: how do you troubleshoot network issues for users you can't physically reach?

Atlassian audit logs carry high-value security and operational signals, yet the raw format makes them hard to use in day-to-day investigations. Nested JSON, arrays inside arrays, and localization keys turn routine questions into slow, manual work. For lean Security and IT teams, that friction shows up as delayed triage, fragile dashboards, and alerts that fire without enough context to act.

This post expands on our analysis from last year that revealed that as much as 20% of IPv4 space has migrated out of Ukraine in the years following the Russian invasion in February 2022. This update reveals that AT&T (a popular destination for Ukrainian IPs) has since implemented a policy ridding itself of customers using AS7018 to originate their routes, often to support residential proxies.

Sentry now supports log drains, making it easy to forward logs into Sentry without any application code changes or manual project-key lookups needed. If your logs already exist somewhere else, you can now see them alongside errors and traces in Sentry, no code changes required. Already want to get started? The quickstart guide is one click away.

Prompt engineering improves single responses, but agent performance is determined by how execution context is captured, replayed, and constrained over time. For the past few years, enterprises have obsessed over prompts, with entire roles emerging around their design and an ecosystem of tooling and templates following close behind. This focus delivered early gains because it allowed teams to rapidly improve outputs without modifying the surrounding system. Over time, those gains flattened.

Generative AI enables teams to write and ship code faster than ever. But current methods for testing and quality assurance have not evolved to match the new pace and scale of deployments. Manual and deterministic testing paths quickly become obsolete when new features are released, and they fundamentally can’t test AI outputs, leaving a massive untested surface area. To keep up, teams need new testing methods that can define what goals users have, and ensure that their outcomes match.

Is OpenShift becoming too expensive or complex for your team? Discover the top 3 OpenShift pain points; from the "pricing inversion" to vendor lock-in and see why agile platform teams are migrating to modular, developer-first alternatives like Qovery.

Discover how Qovery leverages its own platform to accelerate AI development. Learn how an AI specialist deployed a complex stack; including LLMs, QDrant, and KEDA - in just one day without needing deep DevOps or Kubernetes expertise. See how the "dogfooding" approach fuels innovation for our DevOps Copilot.