Operations | Monitoring | ITSM | DevOps | Cloud

The AI red teaming market grew up fast this year. OpenAI bought Promptfoo, Cisco and Microsoft shipped automated attack suites, and a seed-stage startup publicly compromised 50 of 55 live customer service bots. These platforms find real problems at a scale no human team can match. But when you read the findings closely, a pattern emerges: agents talked into refunds, transfers, and data leaks they had standing authority to perform. Patching the prompt fixes one phrasing until the next model update.

London has spent decades establishing itself as one of the world’s major technology hubs. Its strength comes from the concentration of AI and technology startups, financial services and fintech, highly regulated industries, universities and research institutions, international businesses and technology companies, alongside a large community of developers and engineers.

The sovereign cloud conversation has been dominated by physical location and legal jurisdiction. Both matter. But there's a third leg most discussions skip: the software stack itself. If the platform running a sovereign cloud is proprietary code controlled by a company in another country, its sovereignty has a soft underside.

Moving the agent off your machine and pointing it at real clusters — read-only, in shadow mode — then climbing a trust ladder toward carefully scoped action. This is the second article in a three-part series on taking an AI SRE agent from a weekend experiment to enterprise production. Part 1 built a local agent on a throwaway cluster: read-only, propose-only, refined against a small eval set, with portable skills and no production write access.

Picture the scene: you’ve just deployed a rolling update to your service. Half of your pods are running the new version, they all passed their readiness checks, and Kubernetes terminated the old replicas. Suddenly, the new pods start throwing 503 errors. Thankfully, you still have pods on the old version, so you stop the update. If the rollout had been a little bit faster, you’d have an outage. This is the failure mode minReadySeconds exists to prevent.

As businesses evolve in today’s digital landscape, the need for efficient and scalable computing resources has become paramount. In the early days of the Internet, large corporations would build or rent out large data centers to run their applications and serve customers. This was great as they could use dedicated hardware and expand as they pleased.

How long would it take you to get one new line of code into the container running your payment service? In a lot of organizations, the answer runs to weeks, because the change has to clear a build owner, a test cycle, and a release window that nobody wants to open early. That timeline is why so much monitoring advice fails on contact. Most of it opens by telling you to add a library, rebuild the image, and push a new version. If you could do that this afternoon, you would have done it already.

How many containers are running in your production environment right now that nobody can see inside? A vendored service, a compiled binary, an application whose build pipeline left with the developer who wrote it: each one runs, serves traffic, and reports nothing. Instrumenting those workloads means a code change, a rebuild, and a redeploy, and on these containers none of the three are available.