Operations | Monitoring | ITSM | DevOps | Cloud

Production failures don't announce themselves cleanly. They arrive at 2 AM, buried inside 40 million log lines, spread across a dozen microservices, and disguised as something that looks entirely unrelated to the actual root cause. For years, engineering teams absorbed this pain through process: runbooks, on-call rotations, dashboards, and a deep institutional knowledge that lived in the heads of their most senior engineers.

The Harness MCP Server now connects directly inside Google Antigravity. Developers can link Harness in under two minutes and give the agent structured, real-time access to their pipelines, execution history, services, environments, and policies, without leaving the editor. What makes it reliable isn't the connection itself. It's the Harness Software Delivery Knowledge Graph underneath, which gives the agent the context to act accurately, fast, and within your guardrails. ‍

Most hybrid cloud strategies fail not at launch but at scale. The architecture works fine for the first year. The team's workloads are modest, the integration points are limited, and the operational overhead is manageable. Then the business grows. Workloads multiply, data volumes climb, the team expands, and the seams between public cloud and private infrastructure start showing.

AI's environmental cost is real, and it's growing. Training a large language model can consume the electricity of hundreds of households for weeks. Inference at production scale runs continuously, with GPU clusters drawing power around the clock. The data centers that house all of this are some of the most concentrated energy consumers in the modern technology stack.

If you run a platform tools or security team, you have likely heard this request from developers: “I just need a copy of the production database for staging so I can run realistic load and integration tests.” It is a completely reasonable request. Production traffic and data contain the actual request shapes, real-world value distributions, long-tail anomalies, and timing patterns that make tests useful.

If you've ever joined an incident call only to discover the monitor for the affected service was either misconfigured, deleted, or never set up in the first place—you already understand why monitoring as code (MaC) exists.

Across two AWS Summit events in London and New York City, we had the chance to speak with more than 1,000 engineers. They came from startups building their first production stack, and enterprises managing large AWS and multi-cloud deployments. The energy was exactly what you'd expect: major AWS launches, dozens of new service announcements, wall-to-wall cloud conversations. And HAProxy right in the middle of it.

June 19, 2026 This is not a regular monthly update. DataStream Version 2.0 is a milestone — the result of relentless building, learning from customers, and pushing the platform toward what enterprise-scale security operations actually demand. The core has been rebuilt, new capabilities have been added across the board, and the platform is now faster, more resilient, and more extensible than ever. Here’s what’s new.

Choosing a Platform-as-a-Service (PaaS) is not just an infrastructure decision. It is also a decision about how personal data will be handled over the life of the project. It's a governance commitment made early, with consequences that run late. A PaaS does not remove an organization’s accountability for privacy, security, or regulatory compliance. However, a well-architected PaaS can materially strengthen the control environment in which those obligations are managed.

As CTO for Teneo, I get the opportunity to meet with many of our customers to talk about plans for the next few years. I often find we spend a lot of time talking about Digital Employee Experience, but far less time is spent fixing the operational friction that quietly erodes it. Slow devices, degraded application performance, and recurring service desk tickets are common themes in many organizations.