Operations | Monitoring | ITSM | DevOps | Cloud

Today marks an exciting milestone for BigPanda and for the future of IT Operations. We’re thrilled to announce that BigPanda has acquired Velocity, an AI SRE company whose technology and team share our passion for transforming how enterprises keep the digital world running. Velocity brings deep expertise in Site Reliability Engineering (SRE) and major incident response, developed alongside some of the world’s most sophisticated technology organizations.

Across Europe, the cautious optimism business leaders held towards AI agents has evolved into more widespread enthusiasm. What was once a curiosity is now core to how many European organizations operate, respond, and innovate. According to PagerDuty’s latest agentic AI survey, three-quarters or more of organizations in France, Germany, and the UK are deploying multiple AI agents. This growing confidence reflects a broader trend.

The AI SRE landscape has exploded over the past year, with vendors racing to add artificial intelligence capabilities to their platforms. For engineering leaders evaluating these solutions, the sheer number of options can feel overwhelming. Some vendors are building AI-native solutions from scratch, while others are retrofitting AI onto existing workflows. Cloud providers are embedding agents into their ecosystems, and observability platforms are adding intelligence layers to their telemetry data.

Data center vacancy rates in North America have hit record lows, with reports from CBRE and JLL indicating figures between 1.6% and 2.3% as of mid-2025. This is driven by exceptionally high demand from hyperscale and AI users, which is outstripping supply and leading to significant competition for space and power. The tight market is expected to continue through at least 2027, with preleasing of new construction at high levels.

Kubernetes has transformed how teams build and scale applications, but it has also introduced new layers of complexity. Platform and DevOps teams must now integrate and manage multiple technologies: CNI, ingress and egress gateways, service mesh, and more across increasingly large and dynamic environments.

Over the last couple of years, nearly every network and infrastructure observability platform has added the word “AI” to its messaging. Some have introduced helpful capabilities. Others have simply added a chatbot on top of the same dashboards that have existed for a decade. In many ways, the term has started to lose meaning. But inside network operations, the conversation hasn’t disappeared. It has simply become more blunt.

Artificial Intelligence has become the sharpest tool in the digital arsenal – detecting anomalies, predicting failures, and uncovering risks before they unfold. Yet even the smartest system can’t roll up its sleeves and fix what’s broken. AI can see the problem. But only people can solve it. That’s the critical gap in today’s automation revolution: turning AI’s insight into human action.

Nowadays, AI agents are becoming more and more popular and often deployed as part of production systems. However, this rapid adoption brings unique observability challenges that require flexible solutions. On the one hand, AI agents are fundamentally just like any other software services that produce the same classic observability signals we’re familiar with: metrics, logs, and traces.