Operations | Monitoring | ITSM | DevOps | Cloud

AI spend has no ceiling. An engineer can burn $5,000 in an hour, and a team that spins up an agent on Friday can loop it on a bad prompt all weekend. You find out when the bill lands: the money is already gone, the damage pieced back together from logs. Cloud spend had a natural limit. Tokens don’t. Now you see it as it happens. Connect a source and the calls stream in within seconds. Within minutes they’re broken out by model, provider, agent, and user.

Claude Mythos is now available to the public through Claude Fable 5, released June 9, 2026. Claude Fable 5 pricing is $10 per million input tokens and $50 per million output tokens, exactly 2x Claude Opus 4.8 ($5/$25). Claude Mythos 5 (the restricted Project Glasswing version) has identical pricing. Prompt caching cuts input spend by 90%. Batch API pricing is $5/$25 (50% off). In April 2026, Anthropic announced a model it said was too dangerous to release.

The first post in this series argued that AI agent governance hasn’t kept pace with deployment. The second laid out the five pillars of accountability, and what is required. The third walked through why network policies, API gateways, MCP/A2A protocols, DIY security patterns, and Role-based Access Control (RBAC) each leave critical accountability gaps. So what does good look like? The five pillars define what AI agent accountability requires.

You know every service in your cluster by name. You know which team owns each one, what it talks to, how it scales, where its logs go. The agents are a different story. That’s not a criticism, it’s an observation, and it’s one we keep running into. Every company we talk to is shipping agents of some kind, from scales of 10s to 1000s. Customer service bots that field tier-one tickets. Internal copilots that draft emails and summarise meetings and write the boring half of every PR.

Network documentation has a way of becoming everyone’s problem and nobody’s responsibility. Over time, diagrams become outdated, configuration changes go undocumented, and critical knowledge ends up living in the heads of a few senior technicians instead of somewhere the entire team can access it. That’s why organizations are turning to automated network documentation.

AI is changing how teams work. Developers are generating code faster, security teams are automating investigations, and employees across the business are using AI tools to accelerate research, content creation, and decision-making. But this adoption comes with a catch. As usage explodes, it introduces a new set of security risks: a rapidly expanding attack surface, faster attack timelines, potential data exposure, and an alarming lack of visibility into how these tools are being used.

I'm happy to share some really exiting updates from the Flyway product teams this past month.

For Platform Engineering teams, the goal has always been clear: build a secure, scalable internal developer platform that reduces cognitive load and accelerates time-to-market. Yet, a massive obstacle often remains hidden in plain sight: the mainframe. While your distributed teams are shipping cloud-native microservices multiple times a day, your core backend mainframe applications frequently remain locked in an isolated silo, lagging behind on slow monthly or quarterly cadences.

Your Dynamic Host Configuration Protocol (DHCP) server already knows what’s connected to your network. The problem is that DHCP data rarely stays aligned with the rest of your infrastructure systems. Instead, it becomes fragmented across Windows servers, branch offices, spreadsheets, and disconnected operational tools. Lease data ages, assignments go untracked, and teams lose confidence in their network inventory.

During the Toronto KCD (Kubernetes Community Days), I attended an insightful talk on AI resource optimization that highlighted a staggering Gartner study: “AI infrastructure is adding $401 billion in new spending this year alone. Yet, real-world audits tell a much darker story, revealing that average GPU utilization in the enterprise is stuck at a dismal 5%”. While many people in the audience were shocked by that number, the data didn’t come as a surprise to us.