Governing the Agentic Enterprise: Trust, Scale, and ROI

Here’s a prediction that’s rapidly becoming reality: organizations will have more agents than employees.

That’s not some distant future scenario. It’s happening now. And it raises a pressing question for executives: How do you maintain oversight and control when autonomous agents are handling critical business functions at-scale?

Bryan Goode, Corporate Vice President at Microsoft, has a clear answer. You need the same kind of observability for agents that you have for applications and human employees. Without it, you’re flying blind.

The Observability Problem

When agents first appeared, organizations deployed them one at a time. A sales agent here. A support agent there. Maybe a finance agent to handle invoices.

Managing a handful of agents is straightforward. But that’s not where things stay.

As organizations see results, they deploy more agents. Different departments build their own. Third-party agents get integrated. Custom agents get created for specific processes.

Before long, you have dozens or hundreds of agents operating across your organization. And that’s when the problems can start.

Which agents are running? What are they doing? Are there conflicts between agents? Which ones are actually being used? Which ones are sitting idle? Where are the security risks?

Without visibility, you can’t answer these questions. And you can’t manage what you can’t see.

“The most important thing is that every senior executive in a business has what I call observability of the agents that they have in their workplace,” Goode explains.

This isn’t optional. It’s foundational to scale agents successfully.

Agents 365: A Control Plane for the Agentic Enterprise

Microsoft recently announced Agents 365, a control plane designed specifically for this problem.

The concept is simple. Give technology leaders a single view of all agents running across their organization. Let them see conflicts, identify security issues, understand usage patterns, and take action when needed.

“It’s really meant to be a way that a leader, let’s say, a senior technology leader in a company, can look across their entire estate with a single control plane and see all the agents that they have running,” Goode says.

The system shows where conflicts might exist. If two agents are trying to handle the same process differently, that’s a problem. The control plane surfaces it.

It identifies security issues. If an agent has access it shouldn’t have, or if there’s a vulnerability in how it’s configured, the system flags it.

And it tracks usage. Which agents are being used heavily? Which ones are growing in adoption? Which ones have been deployed but nobody uses?

This last point matters more than it might seem.

The ROI Question

When you can see which agents are actually being used, you can measure ROI properly. And measuring ROI from agents requires a different approach than traditional software.

“It depends a lot on the individual function that they focus on,” Goode explains. “Pick a starting point: start with a function like customer service or finance. Then go a click deeper. Pick a business process in that function.”

The key is connecting agents to specific metrics. Measuring AI adoption in general won’t help you understand your ROI. Instead, measure impact on the business processes where agents operate.

For a sales pipeline agent, track pipeline velocity. For a finance agent handling collections, measure days sales outstanding. For a support agent, watch resolution time and customer satisfaction.

“For those processes, you’re going to have a metric,” Goode notes. “As you deploy an agentic business application or an agent into business functions, that’s the measure that you want to watch.”

This approach ties agent deployment directly to business outcomes. You’re not measuring how many agents you have. You’re measuring whether they’re improving the metrics that matter.

The Iteration Pattern

Microsoft learned something important deploying agents internally. Initial results come quickly. But the real gains come through iteration.

“The real results came through iteration,“ says Goode. ” Now that we’ve got an agent doing this, what if we had a human do that? Or what if we added another agent to complement that first agent?”

This iterative approach is critical for both improving results and maintaining oversight.

You start with an agent handling a specific task. You measure its impact. If it’s working, you expand its role or add complementary agents. If it’s not delivering results, you adjust or remove it.

The control plane makes this possible. Without visibility into agent performance and usage, you can’t iterate effectively.

And this is where the deprecation question becomes important.

When to Shut Down Agents

Not every agent that gets deployed stays useful. Some serve a temporary purpose. Others get replaced by better solutions. Some just don’t work as expected.

Knowing when to deprecate an agent is as important as knowing when to deploy one.

“It will help you understand which of the agents in your organization are being most used, and those that are trending upwards in terms of usage. Maybe certain agents served a purpose at one point, but are no longer necessary,” Goode explains about Agent 365.

This matters for several reasons.

First, unused agents represent wasted resources. They consume processing power and require oversight.

Second, they represent avoidable security exposure. While agents are created with appropriate governance and security controls, those that fall out of use should have their access revoked—maintaining data permissions for inactive agents creates unnecessary risk.

Third, they add complexity. Every agent in your environment is something that needs to be managed, updated, and monitored.

The control plane makes deprecation decisions data-driven. You can see exactly which agents are being used and which aren’t.

Security and Governance at Scale

As agent deployments grow, security and governance become more complex.

Agents need access to data to function. But that access needs to be controlled. An agent handling customer service inquiries needs different access than an agent processing financial transactions.

Multiply this across dozens or hundreds of agents, and the governance challenge becomes significant.

The key is applying consistent security models across your entire technology ecosystem. Microsoft’s unified platform enables organizations to extend the same identity and access management that governs human users to their agents. The same data protection policies apply whether a human or an agent accesses information.

“I would assert that tools used for observability of agents should likely be similar, or the same, tools used for observability of applications and the things that us human employees use,” Goode says.

This consistency represents a significant advantage of Microsoft’s integrated approach. Rather than creating separate security frameworks for agents or managing governance across disconnected systems, organizations scale their existing security infrastructure to cover agents. The same Entra ID controls, Purview data protection policies, and Defender monitoring capabilities that secure human access also secure agent operations.

This unified governance model makes security scalable while reducing complexity. Organizations extend the value of their existing security investments rather than building parallel or separate agent-specific frameworks.

Starting Point for Business Leaders

For executives just beginning to think about agent governance, the advice is straightforward: don’t wait.

“If you aren’t moving forward right now, you’re falling behind,” Goode warns. “The most important thing you can do is just get moving.”

But that doesn’t mean deploying agents recklessly. It means starting thoughtfully with a clear process:

Pick one business function. Sales, customer service, finance—choose something specific.

Select a concrete process within that function. Not “improve sales” but “accelerate pipeline qualification” or “reduce quote turnaround time.”

Define the metric that matters for that process.

Deploy an agent and measure its impact on that metric.

Iterate based on what you learn.

This focused approach gives you both results and learning. You see whether agents deliver value in your environment. You understand how to measure that value.  Likewise, you learn what governance challenges emerge at small scale before facing them at large scale.

“Starting somewhere is better than standing still,” as Goode puts it.

Trust Through Transparency

Ultimately, trust in agentic systems comes from transparency. Executives need to see what agents are doing. They need to understand the decisions agents make. They need the ability to intervene when necessary.

This is what the control plane provides. Not just visibility, but actionable visibility. The ability to see, understand, and act.

As organizations move toward having more agents than employees, this governance foundation becomes critical. Without it, scale becomes chaos. With it, scale becomes opportunity.

The technology for agentic business applications is here. The governance tools to manage them responsibly are arriving. The question is whether organizations will build the oversight capabilities they need before the complexity overwhelms them.

The answer, for most, should be to start building those capabilities now.

 

For more information please visit https://www.microsoft.com/en-us/power-platform