Why AI Observability Is Not AI Governance

Governance is the single largest gap in production AI today. 

According to a recent report from Gartner, the average Fortune 500 will deploy more than 150,000 AI agents by 2028, but only about 13% believe they have the right governance in place to support them. 

But what does the “right governance” actually look like for production AI?

If you ask most enterprise leaders what they’re doing for AI governance today, you’ll probably hear something like, “we deployed an AI observability tool for traces and evals.” 

But here’s the million-dollar question: is AI observability really the best solution for AI governance?

When it comes to solving a technical problem, it’s easy to confuse a part of a solution with the solution itself. And nowhere in the governance conversation is that more obvious than in the topic of AI observability.

In this article, we’ll explore the necessary relationship between AI observability and AI governance, why they aren’t just two words for the same solution, and how these two critical technologies actually fit together in an agent governance platform. 

But first things first—what’s the problem we’re trying to solve with AI governance anyway?

Choosing the right solution always begins with defining the right problem.

In a recent interview with the Wall Street Journal, FICO CIO Mike Trkay shared how his org’s 3,500 employees are each creating dozens of new AI agents per day—

“Every day there’s quite literally new agents that are being created, and almost at every tier of the hierarchical structure,” Trkay said.

This rapid expansion of agent resources is what’s known colloquially as “agent sprawl”—and it sits at the very heart of the agent governance dilemma. 

You can think of it this way: every new agent that’s accessing data, spending tokens, and taking action in production environments represents a new governance risk to be managed.

As more and more teams are empowered to build these agents en masse, the complexity of that system expands beyond your ability to see or manage them. 

And it’s in this inverse relationship between complexity and management that the business impact really takes hold. 

Unreliable outputs. Runaway token costs. Data leaks. Fractured ownership. All of these outcomes are symptomatic of an AI ecosystem that’s scaling rapidly without the resources to manage and validate it effectively. 

And like Kubernetes for the application age before it, AI needs a new infrastructure layer to manage and control those risks effectively. 

AI observability is one technology that’s frequently positioned as the panacea solution to these problems. 

But what is AI observability actually? And is it really enough to solve the AI governance gap? 

Let’s find out.

From basic code review to domain-specific analytics and reporting, AI agents are augmenting or outright replacing a variety of tedious and traditionally manual workflows. 

But as more agents begin to take actions independently in production environments, the question of what those agents are actually doing—and whether or not they should be doing it—becomes all the more urgent. 

Inspired by the application observability and data observability categories that came before, AI observability is a specialized technology designed to monitor for, identify, and alert teams about unreliable AI outputs in production.

AI observability consists of two primary features: 

• Traces: a record of what decisions an agent actually made at runtime (tool calls, models, etc). This artifact takes the place of the code within a given agent response. 
• Evaluations: an AI-based monitor to measure the usefulness of a response for a given prompt, usually measured against some predetermined set of rules or examples that create the anchor for scoring the output. 

Unlike traditional software, AI systems aren’t deterministic. When and how a response is created isn’t prescribed in the code; it emerges based on a variety of decisions the agent makes at runtime. And because many of these outputs are unstructured by nature, determining “good” is often less a question of right and wrong and more a question of right and mostly right. 

Traces give us the artifact to understand what our agents did, while evaluations tell us how helpful that was based on the prompt in question. And that’s super helpful. 

Here’s the problem. 

Like all things in software, the application of a tool is just as important as the tool itself. AI observability can tell you when something happens, but it won’t provide you the resources to prevent it from happening—or tell you how to manage it if you don’t. 

And that’s where our definition of AI governance becomes really important.

Pardon the bad analogy for a moment, but it doesn’t matter whether you can see a train coming if you’re already tied to the tracks. 

Unlike observability, governance isn’t primarily concerned with identifying agent problems so much as it is controlling those agents before problems arise. 

The problem with AI observability isn’t that it’s unhelpful—it’s that it’s retrospective. AI observability can give you traces, token counts, latency, and a record of what happened—which is still valuable for sure—but the best trace in the world won’t stop a data breach from happening. And by the time AI observability catalogues the issue, it’s already too late to do anything about it. 

The credential is used. The budget is spent. The data has left the environment never to be recovered again.

The response was right or wrong; the output was safe or unsafe; the spend was efficient or inefficient—but whatever the outcome, AI observability isn’t altering the impact, it’s just writing the epitaph. 

Knowing what happened is absolutely still valuable. But valuable and sufficient to solve the problem are two very different things. 

Without a comprehensive solution to governance more agents will only ever mean more observability alerts. And if you think alert fatigue is bad for applications, I promise it only gets worse for AI. 

AI observability might be part of the solution. But it can’t be the whole solution. 

So what does effective AI governance actually look like?

Governance isn’t primarily reactive. It’s proactive. 

The best trace in the world will only let you watch an agent break free from its constraints. Governance is the layer that decides, in advance and at runtime, what an agent is permitted to touch, spend, and call. 

The reason this distinction matters for a buying decision is that a tool built to identify problems is architecturally different from a tool built to enforce the rules to prevent them. 

True AI governance needs to answer five questions:

• Access and identity. Which systems can an agent reach, with whose credentials, and how easily can that be changed or revoked?
• Model flexibility. Can you run the best model for each task — Claude, GPT, Gemini, open source — or are you locked to one provider's stack?
• Cost. Can you cap or limit spend before an agent burns through a budget, instead of finding out when the invoice arrives?
• Runtime oversight. Can you see what agents are doing, and is that visibility connected to any ability to act on it?
• Domain customization. Can you tailor policies quickly at the user level to accommodate domain-specific risk models?

Observability tells you what an agent did. Governance determines what an agent can do. Most teams treat them as the same problem, and then learn — usually after an incident — that logging isn’t the same as controlling.

The single most expensive mistake for any new technology is treating “observability” and “governance” as synonyms.

Again, AI observability isn’t useless. It’s very useful. But that usefulness needs to be defined in its proper context. 

If you’re sick, a thermometer can tell you that you have a fever, but it can’t make your fever go away—or help you avoid the sick child at the grocery store before you get it.

In the case of agents, governance tells your agents—and your teams—what they can do with AI. Observability tells you when and how they got out of line. And both of these together make it possible to understand and manage your agents at scale.

Observability is a component of a strong governance strategy, but observability is not equal to governance in and of itself. 

Governance doesn’t work without visibility—but the power isn’t in what you see, it’s what you can respond to proactively. 

Most vendors will do one and market the other. But the key to finding a good AI governance platform is finding a solution that will do both simultaneously. 

At Guild, we aren’t stopping at observability or governance. We’re building the first comprehensive agent control plane to manage observability, governance, security, and access controls simultaneously. 

Want to see what real AI governance looks like in practice? Let’s chat.