Guest blog courtesy of JumpCloud.
Organizations worldwide aren’t just experimenting with AI agents anymore. They’re actively making the shift to depend on agents for core operations and workflows, with
72% already having AI agents in production and moving beyond early-stage testing. AI deployments will only continue to mature in sophistication, causing the agent count per person to scale at a rapid pace. This growth is fundamentally reshaping the identity landscape, creating a hard reality for IT and the channel alike. The identity infrastructure previously built for people will no longer work to govern the emerging workforce of agents and non-human identities.
If overlooked, unmanaged agents can cause new identity, access, and audit risks that organizations will need help operationalizing to create secure environments for their customers. To achieve this, channel partners can clearly set a foundation for assessing agent governance through four stages, from visibility to identity and access control to ongoing human review.
Defining a resilient framework
As the AI agent workforce continues to grow, processes such as logging into SaaS tools, calling APIs, moving data between systems, and taking actions no longer require a human operator. 66% of organizations say AI agents have equal or greater system access than human users, resulting in an increase in power and a decrease in control.
There is a real opportunity for the channel here. AI agent governance can be packaged into a repeatable, outcome-driven service that reduces risk while enabling customers to expand automation with confidence.
However, this can’t happen without proper standards in place. Implementing strong guardrails to secure AI agents is essential to organizational success in the modern tech-enabled workforce. This can be achieved in a four-pronged approach:
Discover, Register, Manage, and Govern.Discover: You can’t govern what you can’t see
For channel partners,
discover is the launch point that turns agent governance from a theoretical conversation into an operational one. Through building an inventory of the AI agents operating across an environment, including which tools they connect to, which APIs they call, which systems they can reach, and which workflows they touch, organizations can unlock a deeper level of intelligence. Without that baseline, policy enforcement becomes inconsistent, and incident response can devolve into guesswork. This is especially apparent as organizations report that security, compliance, and lack of visibility and control are now among the top barriers to scaling.
The visibility gap is real, even for teams actively deploying agents. Only 33% of organizations in the testing phase report centralized visibility into agent activity, rising to just 43% among business-critical deployers. That’s why the starting point for any governance program is a full audit of what is running and what it can access, sanctioned and unsanctioned agents alike, regardless of how they were deployed or who owns them.
Register: Enforcing identity and ownership
Once agents are found,
registration is where accountability stops being implied and becomes explicit. In practice, that means creating a formal identity record for every agent. This involves placing it in a central directory, documenting what it does, mapping every system it interacts with, and assigning a named human owner, or custodian, responsible for reviewing access over time.
This is also where many deployments break down. Agents can be spun up quickly, connected to SaaS apps, and left operating long after the original project ends without clear ownership for ongoing access review. This is called a zombie agent. In order to combat zombie agents, governance must treat agents as first-class identities, because agent activity is increasingly embedded inside approved platforms and workflows, not confined to unsanctioned tools.
Manage: Access controls and guardrails
While registration establishes what an agent is and who owns it, the
manage stage is where partners can help customers translate identity into enforceable control. Start with least-privilege access as the baseline, ensuring agents can only reach the systems and perform the actions their function genuinely requires.
From there, reduce exposure by replacing long-lived credentials and shared service accounts with time-bound access wherever possible. For truly sensitive workflows, guardrails are not optional. High-risk actions, like financial transactions, identity changes, and access provisioning, require explicit human approval before execution.
Finally, every AI agent deployment needs a single point of control where access can be revoked immediately across all connected systems if something goes wrong. Just 45% of organizations have set up role-based access policies for individual AI agents, and more than half don’t have a centralized way of revoking access at all. When agents can take high-impact, irreversible actions, teams need the ability to intervene immediately.
Govern: Establishing a process for monitoring and review
Organizations often lack the basics needed for sustained oversight. 59% don’t maintain full audit trails, 55% don’t have a centralized way to revoke access for AI agents, and 33% say the only option is disabling agents manually, system by system. For channel providers, the
govern stage is a managed service opportunity for ongoing review cadences, exception handling, and proving to customers and auditors that agent actions are monitored, attributable, and continuously brought back into policy.
Paving the way for MSP success
The next wave of managed services won’t be defined by how many tools an MSP can monitor. Instead, it will be defined by how well the MSP can impose operational control on autonomous activity inside customer systems. AI agents collapse the distance between automation and privileged access, which means governance must be engineered upfront and maintained continuously, not reactively revisited after an incident or an audit request.
Using the four-phase approach (Discover, Register, Manage, Govern) provides partners with a clean way to simplify delivery and set expectations. If done well, agent governance becomes a durable service motion. This method reliably uncovers and reduces risk, and creates an ongoing program that keeps pace as customers add more agents, more integrations, and more autonomy.
