Containment: Why Your AI Agent Needs Walls
Give your AI agents the walls they need. This video shows why running agents in production takes platform-level containment, not just policy. Watch how Red Hat OpenShift® features like namespace isolation, restricted security context constraints, and NetworkPolicies help prevent unauthorized access and potential data exfiltration.
Why do AI agents need containment?
When you run an AI agent on your laptop or a shared server, it typically inherits your entire environment: network access, files, and credentials. In a personal, low-risk context that might be acceptable. But in a team or production setting, this becomes a significant liability.
All it takes is one compromised agent or one well-crafted prompt injection for an attacker to gain a foothold inside your infrastructure. Many agent frameworks assume a trusted, single-user environment by default. That assumption breaks down as soon as you introduce:
- Multiple agents
- Multiple users
- Sensitive data or systems
Containment—putting clear “walls” around each agent—helps you rethink how agents interact with your systems so that a single failure doesn’t expose everything.
How does OpenShift isolate AI agents from each other?
OpenShift handles isolation at the platform level so you don’t have to bolt it on later. It uses several mechanisms to keep AI agents separated by default:
- Namespace isolation: Each agent can run in its own namespace. One agent’s workloads, secrets, and network traffic are invisible to another’s unless you explicitly configure sharing.
- Security Context Constraints (restricted-v2): This policy enforces non-root execution, read-only filesystems, and dropped Linux capabilities for every container automatically. That reduces what an attacker can do even if they compromise an agent.
By default, this setup helps you reimagine agents as isolated services rather than processes that can see everything on the host.
How does OpenShift limit AI agent risk to the network and cluster?
OpenShift gives you tools to contain both the reach and the impact of AI agents:
- NetworkPolicy: You can implement default-deny egress, so agents can only connect to the specific services they actually need. This helps reshape how agents interact with external systems and reduces exposure if an agent is compromised.
- ResourceQuotas: These quotas ensure that one misbehaving agent cannot starve others in a shared cluster. You can cap CPU, memory, and other resources per namespace or team.
Together, these controls help you run AI agents in a more controlled, production-ready way, instead of giving them broad, implicit access to your infrastructure.
Containment: Why Your AI Agent Needs Walls
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