If you’re building or operating AI systems today, you’re probably facing a familiar dilemma: you’re moving fast, but your governance stack is, at best, an afterthought. Or maybe it doesn’t exist at all. The truth is, you’re not alone.
The speed at which AI has advanced is dizzying. AI agents have leapfrogged from pilot projects or playground demos to being embedded into global production systems. They now answer customer questions, process financial claims, and analyze data to make important business decisions.
In the race to deploy AI, many C-level executives increasingly fear the devil they don’t know, and that the systems they put in place will leave their enterprise susceptible to data drift, data breaches, and critical decisions driven by hallucination. In fact, according to a recent report from IDC, while 68% of companies are investing in agentic AI, only 7.9% of companies believe they are ready to operate AI at scale. That’s a frightening statement about the pace of implementation and the corresponding lack of governance being implemented to ensure its adoption.
Cars and Roads: An Analogy for AI Operations
To better understand AI operations, it helps to consider the relationship between cars and roads. You can build a high-performing car, but it’s only as effective as the infrastructure it runs on. Standard cars require standard roads, but autonomous vehicles need more. Like reflective lane markings, machine-readable signage, vehicle-to-vehicle communication, and reinforced pavement to handle the stress of centralized driving patterns. Try to take your autonomous vehicle off-roading, and it just doesn’t work.
This analogy maps directly to AI. Building a language model or deploying an AI agent is only one part of the equation. Running it safely and effectively, and ensuring it abides by privacy laws, responds accurately, and doesn’t drift into biased or noncompliant territory; requires a robust, purpose-built operational environment and not just a library of advisory PDF documents. Governance tools, alert systems, observability and real-time compliance reporting all form the intelligent infrastructure that modern AI depends on.
In short, we need a new layer in the AI stack. One that governs how AI agents operate, interact, and evolve. Not a checklist or a framework. A control plane.
Building Agents Is Easy. Governing Them Is Hard.
One of the recurring conversations with executives starts like this, “We trust our model, but we don’t trust what happens after deployment.” Organizations aren’t worried about fine-tuning, as much as they are about model drift, policy violations, and whether a misfired response will get them in trouble with a regulator or their customers.
That’s where governance comes in, but not the kind that lives in a PDF. I’m talking about live instrumentation. Real-time observability. Policy enforcement that’s built into the runtime.
There’s a lot of talk right now about open models and transparency, and while we do work with many open models, here’s the truth: even so-called “open” models are effectively black boxes at scale. You’re not going to reverse-engineer a 70-billion parameter model to understand every output. The only practical way to govern AI in production is to instrument the system, not the model.
That means being able to answer:
- Who called the agent?
- What was asked, and how was it answered?
- What guardrails were applied?
- Did any policies trigger alerts?
- Can we trace back decisions to data sources, prompt chains, and context?
If you can’t answer those questions in seconds, your governance posture is performative, not protective.
From Compliance Drag to Deployment Velocity
Traditionally, governance has been framed as something that slows you down. I see it differently. If you build governance into the control plane, you actually move faster, because you’re not stuck revalidating everything, every time you deploy.
Take highly regulated industries for example. For years, compliance meant hiring consultants who prepared reports and returned them months later, and hoping you didn’t miss anything. Now, with the right instrumentation, you can generate HIPAA or NIST compliance artifacts in real time. That’s not a burden; it’s a competitive advantage.
For instance the NIST 600-1 Risk Management Framework for Generative AI (https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.600-1.pdf) is a comprehensive 64 page document that provides guidelines for safe and responsible Agent and Generative AI use cases. This can be viewed as challenging to align to and even harder to maintain adherence to. But, with a control plane and automated report generation, this then becomes an enabler building trust in the system and allowing more widespread business benefit.
Open, Interoperable, and Portable Is Now Non-Negotiable
In terms of architecture, too many AI systems today are locked into closed, single-vendor platforms. Build with vendor X, deploy with vendor X, monitor with vendor X. That model doesn’t scale, and it doesn’t fly with CIOs who are trying to avoid lock-in.
AI in 2025 requires a different approach:
- Any agent: Build it however you want. Bring your own model.
- Any environment: Run it in our cloud, your cloud, or on-prem.
- One control plane: Instrument once, govern everywhere.
Think of it like Kubernetes for AI agents. You build the container. We provide the policy, observability, and compliance layer.
Smaller Models, Bigger Stakes
There’s a strong case for small, local models. They’re more private, cheaper to run, and easier to fine-tune. But they come with their own challenges, especially in terms of governance.
With large foundation models, you often get ecosystem-level tooling. With small models, you’re on your own. That’s why the operations layer matters even more. Whether you’re using GPT-4 or a 1B parameter model trained on internal data, you need the same instrumentation and control infrastructure.
We Don’t Need More Reports. We Need Proof.
One of the most exciting shifts in this space is that AI governance platforms can now become the source of truth for compliance. Instead of generating static documentation, the system itself generates evidence.
We’ve done this for organizations needing HIPAA-compliant audit trails across multiple agents. Every interaction is logged, policy-checked, and ready to export. What used to take six months and $200,000 in manual effort now takes minutes. And this is not a concept. It’s running in production.
The Future Is Autonomous. And Accountable.
We’re entering a world where AI agents are as embedded in operations as APIs or microservices. But without a control plane, we’re flying blind. The risks are real, but so are the opportunities if we treat governance as infrastructure and not as an afterthought.
If you’re building AI systems, especially in high-impact domains, it’s crucial to get started today: instrument your stack, demand interoperability and make governance a keystone in your architecture.
Author Bio:
Mark Stadtmueller is Chief Technology Officer at SUPERWISE, where he leads the company’s efforts around AI governance and operational infrastructure. A veteran of multiple enterprise tech transformations, Mark advocates for systems that scale responsibly, enable innovation, and protect user trust in an increasingly autonomous digital world. You can try Superwise free for 30 days to explore its AI governance and operations capabilities: superwise.ai/pricing
