Do you know what your agents are doing? How to improve agentic AI visibility

When I talk with enterprise leaders, I often ask a simple question: How many AI agents are running in your environment right now? The most common answer is usually some version of, “We’re not really using AI agents yet.”

In many cases, that answer doesn’t hold up under closer inspection.

If your organization has adopted AI-powered automation over the last couple of years, there’s a good chance agent-like systems are already at work somewhere in your environment. 

They may be embedded in customer support workflows, internal knowledge systems, reporting pipelines, developer tools, or business process automations. They may even be sitting inside low-code platforms or third-party products that teams adopted to move faster.

They’re answering questions, summarizing documents, routing work, generating recommendations, calling tools, and influencing decisions that affect real systems and real people. Some were deployed intentionally. Others started as experiments, solved a short-term problem, and quietly stayed in place.

That is what makes this moment important: Humans and AI systems are already working side by side in many organizations. The bigger question is whether leadership has visibility into what those systems are doing, what they can access, and how much responsibility they have.

Most organizations have a decent handle on traditional cloud infrastructure. They know what services are running, who owns the major platforms, and where the monthly spend is going. They may not have perfect visibility, but they have enough to manage core production systems with some level of discipline.

AI agents are different because they rarely appear as a single, clearly defined application.

An agent is usually a collection of moving parts:

• There may be a model behind it, often accessed through an API. 

• There may be tools it can call, such as internal services, databases, SaaS applications, file systems, or external APIs. 

• It may rely on prompts, orchestration logic, retrieval layers, memory, permissions, and workflows that determine how it behaves in practice.

In the cloud, all of these pieces appear separately. You can see compute usage. You can see storage, logging, API calls, and network traffic. What you often do not see is the full system represented in a way that makes the agent visible as an operational unit.

That is where organizations get into trouble.

If no one documents the full workflow, assigns ownership, and treats the agent like a real production capability, it blends into the background. It looks like infrastructure, automation, and a few connected services doing their jobs.

That makes it easy for agents to end up in production without clear oversight. Not because anyone was reckless, but because the system was never visible in a way that leadership could actually manage.

Without proper oversight, agentic workloads can rack up costs and impact major business decisions.

If leaders don’t know what agents are running, they can’t confidently say whether those systems are delivering value. That becomes a real problem once AI-related spend starts growing and teams are asked to explain what the organization is getting in return.

I see this pattern often: A team identifies a useful business problem and builds an AI-driven workflow to improve it. Early results look promising. The project gets attention. Time passes, the system stays in place, and the costs continue to show up. 

When someone asks what business outcome the agent is currently driving, the answer is often vague. The system may still be functioning exactly as designed. But that doesn’t mean it’s still valuable.

Business processes change. Teams change. Priorities change. The person who originally understood the system may have moved on. The workflow around the agent may have evolved while the controls around it stayed the same. Over time, the connection between the agent’s activity and the reason it was deployed becomes harder to explain.

At that point, the problem is no longer just technical. It is operational. And it is one of the easiest ways for enterprise AI investments to drift without anyone noticing right away.

Cost usually gets leadership attention first, but it is far from the only concern.

Agents do more than generate content. They influence workflows. They make choices within boundaries. They trigger actions. They shape what happens next in a process, sometimes with real consequences.

In some cases, the stakes are relatively low. An agent might summarize a document, classify a request, or draft a response for a human to review. In other cases, the stakes are much higher. An agent might escalate an issue, modify a record, send a communication, trigger a downstream process, or recommend an action that someone treats as authoritative.

When no one is watching closely enough, problems tend to show up after the impact is already visible.

This is the part many organizations underestimate. It’s one thing to monitor whether a service is healthy. It’s something else entirely to understand what an agent decided, what information shaped that decision, what tools it used, what action it took, and what happened as a result.

That level of visibility is what separates an interesting AI experiment from a system you can actually operate responsibly in production.

And the challenge gets harder as organizations move into more connected patterns. Once agents start interacting with multiple tools, shared data sources, and other systems, the number of potential failure points increases rapidly. So does the need for real ownership, operational discipline, and AI agent governance.

If agents are already participating in business workflows, leaders need to be able to answer a few basic questions.

• What AI agents are currently operating in our environment?
• What can they access? What tools can they call, and what actions can they take? 
• Is there a human owner accountable for each agent?
• Do we have monitoring that shows what the agent is actually doing?

In many organizations, the answers are incomplete, scattered, or dependent on whichever person happens to remember how a particular system was put together.

That is usually a sign that agent adoption is moving faster than operational maturity.

The good news is that this is not a tooling problem. The architecture patterns exist. The observability approaches exist. The AI governance conversations are happening.  What’s missing is managing agents the same way we manage humans and other production systems.  

Many organizations still treat agents like side projects even though they have access to production data, increase costs, and shape business outcomes. This approach creates blind spots that affect costs and risks, while accountability questions rise. 

Agents are now a part of your environment. They need ownership, visibility, and oversight. They need to be evaluated not just on whether they work, but also on whether they remain appropriate, effective, and valuable.

That is part of what modern technical leadership looks like now. Leading teams today means leading both the people and the systems they put into motion. Even when an agent is doing part of the work, humans are still responsible for the outcome.