Red Hat

Your agent works. It reasons, calls tools, and returns answers in the demo that impress everyone. But between that notebook and a production deployment sits a gap having nothing to do with your model or your framework. Three failures hit a single AI agent deployment overnight—43 duplicate tickets, $4,000 charged to the wrong account, and a hallucinated refund policy leading to a $280 return the company had to honor. The agent ran on LangChain. It worked perfectly in staging.

Today, Windows Server environments power critical internal portals, application programming interfaces (APIs), and web applications. Every one of them depends on security certificates that expire on a schedule your operations team didn’t choose. When renewal slips, services go dark. When rotation happens at the wrong moment—during peak business hours, mid-deployment, or in the middle of a compliance audit freeze—a routine update becomes a major incident.

I just got back from HL7 FHIR DevDays, a developer conference focused on Fast Healthcare Interoperable Resources (FHIR). I was there to talk about AI transparency in health data and using multi-agent AI to suggest useful care plans for patients. I have been part of the HL7 community for 6 years, working to make sharing health data a reality. But I have been a software architect for a lot longer, almost 30 years in the industry.

Policy enforcement has moved to the forefront. Let’s break down why this is the case and how policies can help you improve governance as you stay in control of operations. First, let’s examine your current production environment. Compliance, governance, and security aligned with specific standards represent an ongoing and essential need. In fact, some standards come with costly daily fines for each day an environment is out of compliance.

Modern IT environments are complex. Development and operations teams must constantly balance resolving current infrastructure challenges with planning for scalable, future growth. This requires deep product expertise and technical skills from internal teams that are already resource-constrained. To bridge this gap, enterprises use Red Hat Technical Account Managers (TAMs) as an extension of their teams.

When people think about AI infrastructure, most attention naturally gravitates toward model training. Training large models requires massive datasets, distributed compute, and specialized hardware accelerators. The engineering involved in orchestrating training jobs across clusters of graphics processing units (GPUs) or tensor processing units (TPUs) is significant, and it's often the most visible part of the AI lifecycle. Inference, by contrast, appears deceptively simple.

The managed services market is navigating an unprecedented inflection point: For many service providers, recent changes in virtualization licensing and vendor partner tiers have elevated costs from a standard operational challenge into an existential threat to business continuity. For highly regulated industries like financial services and healthcare, this shift is even more complex.

Managing virtualized workloads alongside containerized applications remains a persistent challenge for IT operations, often creating siloed management environments. At Red Hat Summit 2026, I had the opportunity to take the stage during the OpenShift Spotlight session and demonstrate how Red Hat OpenShift is bridging this divide, by allowing organizations to treat a virtual machine (VM) and a container as first-class citizens on a single platform.

AI clouds have advanced beyond initial pilot testing, and the challenge now isn’t simply configuring the physical hardware. Organizations must focus on operating an efficient shared platform that provides predictable operating costs, access to the latest computer chips, and ongoing platform updates without relying on fragile custom code or complex software adjustments.

Uber burned through its entire 2026 AI tools budget by April. Microsoft faced a similar crisis, pulling Claude Code licenses because the tool worked too well and people used it too much. Even OpenAI's chief executive officer (CEO), Sam Altman, has called token costs "a huge issue" for the company and its customers. "Tokenmaxxing," the tendency to burn through tokens without a clear link to business outcomes, has gone from an internal joke to a boardroom problem.