How to Manage AI Agents at Scale Without Losing Control

AI agents are moving from experiments to production faster than most organizations expected. Teams are spinning up agents across engineering, ops, and support every week. That's the good news.

The bad news: most organizations have no idea how many agents are running, who owns them, what they can access, or what they cost.

This is shadow AI. It's the new shadow IT, but faster and with more access to critical systems.

At Guild, we're building the runtime and control plane for AI agents. We spend a lot of time talking to teams who are navigating this transition from experimentation to production. Here's what we're seeing.

A year ago, AI agents were mostly experiments. Today, non-technical users are building them too. That's a good thing. But it also means agents are being spun up across teams without central oversight.

The pattern looks like this:

1. Someone builds an agent that automates a painful workflow
2. It works, so others start using it
3. It gets connected to more systems like CI/CD, databases, credentials, and ticketing tools
4. No one knows who owns it, what it can access, or how often it runs

We call this agent sprawl. And without a system to manage it, you're inheriting risk you didn't sign off on.

Cost

Agents burn through budgets fast when misconfigured. We've heard this story multiple times: an agent set to run every minute instead of daily. That's a 1,440x multiplier on LLM costs before anyone notices.

Without visibility into frequency, token usage, and compute, cost surprises become the norm.

Security

When agents touch production systems, basic questions matter: Who approved this? What credentials does it use? What can it read and write? What did it actually do?

Most organizations can't answer these questions today. Agents are built in isolation, connected ad hoc, and run without audit trails. Security teams inherit risk they didn't sign off on.

Visibility

Ask most platform teams how many agents are running in their org. The honest answer is usually: "We don't know."

There's no inventory. No ownership. No way to understand what's running, what it's doing, or whether it's still needed.

Managing agents at scale means treating them like production software. That requires a few things:

Governance on who can create and publish agents

Not everyone should be able to deploy an agent to production. Organizations need clear policies on who can build agents, who can publish them, and what review process they go through before they touch real systems.

Permissions at the tool and workspace level

Agents shouldn't have blanket access. Permissions should be scoped by tool, by workspace, by environment. An agent that reads from a staging database shouldn't automatically have access to production.

Review and approval workflows

Before an agent goes live, someone should sign off. That means approval workflows, staged rollouts, and the ability to roll back when something goes wrong.

Visibility into agent inventory, ownership, and activity

You can't manage what you can't see. Organizations need a single view of every agent: who owns it, what it can access, how often it runs, and what it costs.

Cost awareness that scales with usage

As agents multiply, so do costs. Teams need dashboards that show what agents are doing, what they're spending, and projections for what happens if usage continues to grow.

The answer isn't to slow down agent adoption. It's to build the infrastructure that makes it safe to move fast.

That's the idea behind a control plane for agents. A single layer that provides:

• Inventory and ownership for every agent
• Granular permissions and approval workflows
• Logging and observability for every session
• Cost visibility and alerts when things spike

Think of it like GitHub for agents: versioned, permissioned, observable, and improved together across teams.

This is what we're building at Guild. We believe agents should be treated as shared infrastructure, not isolated experiments. Versioned, governed, and evolved together.

You don't need to govern every agent on day one. Start with what matters:

1. Identify high-risk agents. Anything touching production systems, credentials, or customer data.
2. Define ownership. Every agent should have a clear owner responsible for its behavior.
3. Pilot with 1-2 workflows. Internal automation, ticket triage, or order management are good starting points.
4. Build the muscle before scaling. Get the process right, then roll it out.

The goal isn't to lock things down. It's to create the conditions where teams can move faster because they're not worried about breaking things

Agents are powerful. They're also a liability without control.

The companies that win will be the ones who treat agents like production software, with versioning, permissions, and oversight. Not because they're scared of AI, but because they've seen what happens when automation runs without guardrails.

The question isn't whether to adopt agents. It's whether you're ready to manage them.

At Guild, we're building the runtime and control plane to help teams make that transition. If you're thinking about how to move agents from experimentation to production safely, we'd love to talk.

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